US20160063699A1 - Mammography information system - Google Patents

Mammography information system Download PDF

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US20160063699A1
US20160063699A1 US14/935,709 US201514935709A US2016063699A1 US 20160063699 A1 US20160063699 A1 US 20160063699A1 US 201514935709 A US201514935709 A US 201514935709A US 2016063699 A1 US2016063699 A1 US 2016063699A1
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images
image
database
abnormality
patient
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US14/935,709
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Gregory A. Gustafson
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Penrad Technologies Inc
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Penrad Technologies Inc
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Definitions

  • the invention relates to management of medical data and more specifically to patient data and breast tissue images originating from multiple modalities.
  • a radiologist generally begins his or her review process by reviewing a patient's background information relevant to a radiology study, such as a patient's name, age, and any applicable medical conditions or risk factors. After reviewing the background information, the radiologist views multiple images created by radiological, X-ray, computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), tomosynthesis, or other imaging technique of the patient's breast, or other organ, and dictates or uses a computerized information system to track findings, create reports, and make recommendations for future examinations. Such findings can include information pertaining to tissue density, the presence of masses, cysts, calcifications and other abnormalities, or any other breast tissue characteristics.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • MIS computerized mammography information system
  • PenRad Mammography Information System available from PenRad. This system provides for the digital presentation of patient data.
  • BI-RADS Breast Imaging-Reporting and Data System
  • ACR American College of Radiology
  • BI-RADS provides a system of mammography assessment categories in the form of standardized codes assigned by a radiologist during or after the viewing and interpretation of a medical image.
  • BI-RADS allows for concise and unambiguous understanding of patient records between multiple radiologists and medical facilities. Consequently, a large number of mammogram images, biopsy results, and diagnosis statistics are potentially available in a patient-anonymous format, in compliance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA).
  • HIPAA Health Insurance Portability and Accountability Act
  • DICOM Digital Imaging and Communications in Medicine
  • This format provides for the distribution and viewing of medical studies and images across a variety of platforms.
  • the use of DICOM has, among other things, enabled industry compatibility and improved workflow efficiency between imaging and other information systems located in various healthcare environments.
  • the DICOM standard is an 18-part publication, PS 3.1-2008 through PS 3.18-2008 describing a standard for digital imaging and communications in medicine developed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA), which is hereby incorporated by reference in its entirety.
  • ACR American College of Radiology
  • NEMA National Electrical Manufacturers Association
  • the DICOM standard provides a method of uniquely numbering any image or other information object to facilitate the unambiguous identification of images or information objects as they are viewed or manipulated in a system or transported across a network.
  • DICOM server Conventional imaging systems enable a DICOM server to provide medical images across a network to various DICOM compatible clients on the network.
  • DICOM clients include picture archiving and communications systems, softcopy workstations, computer-aided diagnosis (CAD) systems, DICOM compatible CD or DVD burners, and other network system devices known to those skilled in the art.
  • CAD computer-aided diagnosis
  • DICOM compatible CD or DVD burners and other network system devices known to those skilled in the art.
  • U.S. Pat. No. 6,909,795 to Tecotzky et al.
  • Embodiments of the invention relate to systems and methods of retrieving and analyzing patient data in a mammography information system as part of or in conjunction with the diagnosis and interpretation of patient mammography images that substantially meet the aforementioned needs of the industry.
  • the system is capable of retrieving, presenting, and analyzing patent images originating from a variety of modalities.
  • a configurable mammography diagnostic system comprises a plurality of electronic displays, at least one of the plurality of electronic displays configured to display a breast tissue image having at least one region of interest, a database including a plurality of existing categorizations of at least one known region of interest in at least one of a plurality of breast tissue images, a graphical user interface presented on at least one of the plurality of electronic displays and including an anatomical diagram on which the at least one region of interest can be marked, a detailing button linked to a screen configured to present a plurality of possible characteristics according to which a manual current categorization of a region of interest in the breast tissue image can be defined, and a profiler display button configured to present statistical information related to a comparison of the manual current categorization with the existing categorizations, a clipping tool with which a portion of the breast tissue image displayed on at least one of the plurality of electronic displays can be selected as a second image, the second image displayable on at least one of the plurality of electronic displays as
  • a method for managing patient mammography data comprises obtaining a plurality of breast tissue images selected from the group consisting of an X-ray image, a CT image, an MRI image, an ultrasound image, and a pathology image, identifying a region of interest in at least one of the plurality of breast tissue images, obtaining a categorization of the region of interest according to an established lexicon, comparing the categorization with a database of existing categorizations and presenting a diagnostic indicator based on the comparing, storing a selected region of the at least one of the plurality of breast tissue images as a second image, mapping the second image to a storage location of the at least one of the plurality of breast tissue images, and associating the selected region with the categorized region of interest.
  • a mammography information system comprises at least one electronic display, a graphical user interface presented on the at least one electronic display and configured to present data and information related to a patient, the graphical user interface comprising an image gallery configured to display thumbnail representations of a plurality of images that form a portion of the data and information, the plurality of images selectable from X-ray images, CT images, MRI images, ultrasound images, pathology images and document images, and a database operable to store the thumbnail representations of the plurality of images.
  • FIG. 1 is an example mammogram information system (MIS) display workstation according an embodiment of the invention.
  • MIS mammogram information system
  • FIG. 2 is an example of a mammography exam data-form suitable for use with embodiments of the invention.
  • FIG. 3 is an example of the mammography exam data-form of FIG. 2 indicating a region of interest (ROI).
  • ROI region of interest
  • FIG. 4 is an example of a mammogram image with an ROI indicated.
  • FIG. 5 is an example of an ultrasound image with an ROI indicated.
  • FIG. 6 a is another example embodiment of a ROI data entry form for use with embodiments of the invention.
  • FIG. 6 b is the ROI data entry form of FIG. 6 a with additional ROI categorizations entered.
  • FIG. 6 c depicts two additional exemplary embodiments of ROI data entry forms for use with embodiments of the invention.
  • FIG. 7 is an example of a form showing the statistical analysis of a ROI.
  • FIG. 8 is an example of a form showing available images that match statistical analysis of the ROI of FIG. 7 .
  • FIG. 9 is an example of a form showing a patient's exam history.
  • FIG. 10 is an example embodiment of a report generated according an embodiment of the invention.
  • FIG. 11 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 12 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 13 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 14 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 15 is an example of a mammography exam data-form and an example of a ROI gallery window.
  • FIG. 16 a is an another depiction of the ROI gallery window of FIG. 15 .
  • FIG. 16 b is another example of a ROI gallery window for use with embodiments of this invention.
  • FIG. 17 is an example embodiment of a ROI viewer depicting an individual image for use with embodiments of this invention.
  • FIG. 18 is an example of an interpretation work-list form for use with embodiments of the invention.
  • FIG. 19 is an example of a prior examinations form for use with embodiments of this invention.
  • Embodiments provide a computerized mammography information system that allows for the digital correlation of a wide variety patent data related to a mammography image or other breast tissue diagnostic imaging procedures.
  • An exemplary system is able to electronically track breast tissue abnormalities across multiple image types, provide a customizable interface for convenient and efficient image review, allow for an individual user to save preferred image hanging protocols, categorize multiple imaging types, generate statistics, and provide patient correspondence.
  • CAD computer aided diagnostic/detection
  • This advancement in accuracy also provides the benefit of reducing the need for expensive and invasive biopsy or surgery due to false positive diagnosis.
  • Embodiments of the mammography information system provide an efficient, easy to use, and customizable interface for use by a medical professional for the review and analysis of medical images from a variety of source.
  • the system is capable of integrating medical images acquired through X-ray, CT, ultrasound, MRI, tomosynthesis, or other imaging techniques.
  • the availability of this collection of breast tissue images and their associated biopsy results presents an opportunity for statistical analysis of the likelihood that a matching region of interest (ROI) in an individual patient's mammography images is malignant or benign and whether or not a biopsy or further imaging should be ordered. Therefore, there is a need for a system that will quickly allow a radiologist to classify a ROI in a mammogram or other image and correlate the ROI to a large pool of existing data samples that have been definitively diagnosed in order to improve the accuracy and efficiency of patient diagnosis.
  • the radiologist can be assisted in the classification of the ROI by a CAD module by automatically detecting potential ROI abnormalities or simply reducing the number of physical or verbal actions needed by the radiologist to enter the ROI classifying data.
  • a MIS is provided for use by a radiologist or other medical professional that preloads all of an individual patient's medical images for a specific portion of the patient's anatomy, regardless of the modality used to create the images.
  • a CAD module for the appropriate image type can isolate one or more ROI for review in an individual image.
  • the disclosed invention takes these individual CAD results and correlates any common ROI findings between images of the same or different modalities.
  • a summary “map” or outline of the examined patient's anatomy is then generated and displayed for the medical professional along with any other details about the potential ROI(s) that were generated by the CAD module(s).
  • the mammography image gallery and clipping system provides a convenient organization of all of the images associated with a ROI, regardless of modality, for presentation to a medical professional.
  • the system stores lower resolution clippings, or thumbnail images, for pathological images, reports, and abnormalities found, and optionally categorized, by radiologists or CAD products at a facility that have been entered into a mammography information system.
  • the system stores low resolution images as well as the reference to the original image and ROI of the original image. As more patients are definitively diagnosed and their pathology records updated in the system, the larger the collection of abnormality images depicting a previously diagnosed and imaged condition that become available in the system.
  • This system can be integrated into an existing MIS or utilized as a standalone interface providing access to a large sample of mammogram abnormality images.
  • the system also provides an efficient mechanism for creating a comprehensive collection of abnormality data.
  • the collection comprising a uniform lexicon of classifications that allows for further analysis and study of the data while still maintaining patient privacy as required by the applicable law.
  • Those skilled in the art of developing and maintaining electronic databases will appreciate and understand the tradeoffs associated with the storage requirements necessary for the implementation of the contemplated system. As numerous mammography facilities implement this non-patient identifying (and HIPAA compliant) data can be transferred to a central location accumulating a more complete database of abnormality images and the corresponding characterization of data points for various pathology types.
  • the method of analyzing and retrieving abnormality tracking data provides a report of the statistical frequency of diagnosed patients both locally and nationally with mammogram ROI classifications similar to an individual patient.
  • the abnormality data can include information disclosing the frequency of similar ROI classifications have been biopsied and the number of biopsies that were malignant or benign.
  • the disclosed method of capturing and reporting abnormality tracking data provides a radiologist or other medical professional a tool to assess the likelihood of a ROI being malignant or benign, and whether or not the patient should undergo additional testing. The system then presents these statistics to the radiologist who can then choose to look further into the underlying related data if he or she desires.
  • the statistical mammography predictive system provides instantly and continually updated outcome statistics to a medical professional.
  • the system utilizes the information and data points for each and every abnormality found by radiologists at a facility that have been entered into a mammography information system. As more patients are definitively diagnosed and their pathology records updated in the system, the greater the chances that an individual patient will have a condition similar to a previously diagnosed and imaged condition.
  • This system can be integrated into an existing MIS or utilized as a standalone interface providing access to a large sample of mammogram abnormality data.
  • the system also provides an efficient mechanism for creating a comprehensive collection of abnormality data.
  • the collection comprising a uniform lexicon of classifications that allows for further analysis and study of the data while still maintaining patient privacy as required by the applicable law. Only unique copies of each combination of tracing data points must be kept in the system. As duplicate data is accumulated the counters of the abnormality and its diagnosis as benign or malignant are incremented. This aggregation of data creates a compact and anonymous abnormality database for the medical location. If desired, a complete reference of all abnormality data can be maintained. Those skilled in the art of developing and maintaining electronic databases will appreciate and understand the tradeoffs associated with the storage requirements necessary for the implementation of the contemplated system.
  • this non-patient identifying (and HIPAA compliant) data can be transferred to a central location accumulating an more complete database of abnormalities and the corresponding benign or malignant counters for each combination of tracking points and pathology. Therefore, the large number of recorded abnormalities can be culled down to a manageable set of unique combinations specified by radiologists around the country. This culling, or grouping of duplicate abnormalities, allows for a medical professional to access a comprehensive database of the known set of abnormalities nearly instantaneously.
  • system disclosed provides a mechanism to evaluate, validate, and improve any of a variety of existing CAD modules and techniques by providing an efficient platform for testing the cad module or technique against a wide variety of known, physician evaluated, and definitively diagnosed, patient abnormalities or ROI.
  • FIG. 1 illustrates an example embodiment of a mammogram display workstation 100 .
  • a typical mammogram display workstation 100 includes a controller display system 110 and at least one high-resolution image monitor 112 .
  • One or more additional high-resolution image monitor units 114 can also be used to provide additional viewing area to provide for the comparison of two or more images at full resolution.
  • the controller display system 110 is any of a variety of commonly available video display monitors coupled to a personal computer such as an IBM-PC or compatible system running a version of the Microsoft WINDOWS operating system, or the equivalent thereof.
  • the image monitors 112 and 114 are liquid crystal displays (LCDs) that provide high-resolution and enhanced contrast for ease of viewing images, but may also be a cathode ray tube or other appropriate display in other embodiments.
  • An exemplary image monitor can display approximately 2500 ⁇ 2000 pixels, although a variety of image monitor sizes are contemplated.
  • the mammogram display workstation 100 includes a server computer (not shown) that runs DICOM communications components of the mammogram display workstation 100 ; alternatively, this DICOM software may run on the controller display system 110 .
  • a server computer is included that runs an Archived Image Retrieval service; alternatively, this software may also run on the controller display system 110 or on the DICOM compliant server.
  • the mammogram display workstation 100 includes software that allows images to be analyzed using the image processor in the controller display system 110 to analyze each image of a study set, compare with complementary images to generate a suspect list to reduce false indicators, and to generate graphic overlay images to identify areas of interest.
  • imaging tools included in the system allow a user working with the system to further manipulate an image.
  • These software tools may provide magnification of a desired region of an image; image inversion, reversal, rotation, or other repositioning; image/background color inversion; noise filtering from images to reduce or eliminate extraneous data and enhance pertinent image data; customized side-by-side image comparisons; and image reorganization, for example.
  • FIG. 2 illustrates an example embodiment of a medical diagnostic system that includes an abnormality-summary window 200 .
  • Abnormality-summary window 200 provides a convenient patient information summary 210 and an interface to import or enter additional data.
  • the radiologist can enter abnormality data for either the left or right breast by clicking on an “Add Abnormality” button 220 .
  • a user can import a CAD report detailing any abnormalities that have been detected by existing CAD software. Examples of suitable CAD software include the CadStream product by Confirma and the B-CAD product by Medipattern, among others.
  • imported CAD information stored in compliance with a pre-determined system can be used to generate a wire-frame map or guide 230 depicting the location and depth of a ROI in or on a patient's anatomy that was detected by the CAD software or entered manually by a radiologist.
  • the density of the patient's tissue is also presented in selector 240 .
  • the guide 230 includes both a craniocaudal (CC) view 250 and a mediolateral/oblique (ML) view 260 of both the left and right breasts of a patient.
  • the ROI is depicted by the craniocaudal mark 252 and the mediolateral mark 262 .
  • an abnormality may only be visible in one or the other of the ML or the CC view and, accordingly, only a single mark would be displayed in either the craniocaudal (CC) view 250 or the mediolateral/oblique (ML) view 260 .
  • the ROI data underlying either craniocaudal mark 252 or mediolateral mark 262 can be represented as the number of pixel spaces from at least two edges of the original image represented by the ROI.
  • the retention of the number of pixels from at least two edges provides for the derivation of the location of the ROI on the original image. This allows the storage of multiple ROI for a single high-resolution image without the need to store multiple copies of the high-resolution image or even high-resolution clippings. It also permits derivation or mapping of an ROI in one image to other images based on known pixel sizes and edge distances.
  • the data underlying these two marks are used to then calculate an approximate location of the abnormality as viewed by a physician when facing the patient. This calculation also compensates for the fact that during the creation of a mammography image, the patient's breast is compressed to increase the amount of viewable tissue in the two-dimensional x-ray image. Additionally, compensation must be made for the angle at which the mediolateral/oblique view 260 is taken relative to the craniocaudal view 250 during mammogram imaging. Those skilled in the art will appreciate that the two views are not necessarily created at angles exactly perpendicular to each other due to the wide variety of patient anatomy and the need to capture as much tissue as possible in each image. The breast orientation, size and thickness information is provided along with the mammogram image.
  • the resulting combination of the craniocaudal data and the mediolateral data produce the clock-position 270 as shown for the exemplary ROI.
  • This calculation is not calculated if the ROI is only visible on a single image, as both a craniocaudal and mediolateral position are required, along with a distance either from the patient's nipple or chest wall, to calculate the location of the ROI in three-dimensional space.
  • An abnormality does not need to be located or seen in both views to be characterized. Often in mammography an abnormality is only seen in one view and additional imaging is conducted to confirm its location in another view. The additional imaging can also reveal superimposed tissue, a situation in which the breast tissue of several layers was compressed together causing a potential mass seen in a single image with the appearance of an actual abnormality. A radiologist viewing multiple images of the same tissue area can appropriately categorize these situations.
  • a three-word indication 272 of the approximate location of the ROI in the patient's breast is located in the inferior (lower), lateral (outside), middle (distance between the chest and nipple) portion of the patient's right breast. Similar terms for the remaining quadrants and depth are provided by the ACR guidelines and will be understood by those skilled in the art.
  • An additional feature of the system is the capability of importing any ROI from a patient's previous examination that are already present in the system's database.
  • a radiologist or technician can select the “Clone Prev” button 280 to review and import data from a previous examination. This feature further eliminates the need for duplicated effort on the part of the medical professional conducting the review of the patient's exam images.
  • FIG. 4 depicts an exemplary x-ray generated mammogram image 300 with an ROI indicated by a dashed outline 310 on the image 300 of the patient's breast tissue 320 .
  • FIG. 5 depicts an exemplary ultrasound image 330 with an ROI indicated by a dashed outline 340 on the image 330 of the patient's breast tissue 350 . While the type of information depicted in a mammogram image 300 is clearly different from the ultrasound image 330 , the system maintains the ROI indicated on each respective image by storing the coordinates of each ROI as an offset, in one embodiment utilizing the number of pixels, from at least two edges of the original digital image, regardless of the technique employed to generate the image.
  • FIG. 6 a depicts an embodiment of an abnormality-detailing window 400 .
  • the detailing window 400 provides an interface for a radiologist to enter or view the detailed attributes that describe an abnormality in a selected ROI.
  • FIG. 6 a depicts the single attribute 402 of a “Mass” as being selected to describe the ROI depicted in FIG. 3 .
  • the presence of a mass alone is generally not enough to indicate the presence of a malignancy.
  • the radiologist selects an impression 404 and an appropriate recommendation in the “Impression & Recs” area 406 .
  • the system suggests an impression or recommendation in area 406 based on other selected attributes in window 400 , which can then be reviewed by the radiologist and altered, if desired.
  • the system can also dynamically and automatically adjust the selection in area 406 if other attributes in window 400 are changed during review.
  • area 406 is selectable by a radiologist or doctor.
  • the abnormality-detailing window 400 can include a profiler button 410 that provides a count of matching abnormalities and their pathological outcome.
  • the profiler button 410 or another appropriate window, displays the number of biopsies performed that were diagnosed as malignancies 412 , the number of biopsies performed that were diagnosed as benign 414 , and the total number of matching abnormalities 416 in the database.
  • the sum of the number of malignancies 412 and the number of benign 414 is the total number of biopsies performed on abnormalities possessing the same attributes selected in detailing window 400 at that location.
  • the second line 418 of profiler window 410 displays these same quantities found in a national database. As discussed above, the single attribute of a Mass 402 in FIG.
  • the profiler button 410 is depicted in the lower corner of the screen to provide a convenient, yet out of the way area to present statistical information. Other locations or embodiments, such as a floating window that can be repositioned by the radiologist are contemplated.
  • Two database versions are typically present in every system—one is the “local” version containing the data specific to the medical facility where the system is installed. This local data can be subsequently uploaded to a centralized server to be integrated with into a “regional,” “national,” or “global” version of the database. This allows individual users to compare their own facility's results with a larger sample of results. Additionally, the “local” version can be linked to the on-site examination image data, allowing the radiologist to see other examinations related to a specific pathology finding or set of characteristics. The radiologist can then nearly instantly view selected examinations, images, or specified regions of interest retrieved from the local database. The system can also be configured to link to information and retrieve images from the larger databases, although in one embodiment this can be done without any patient identifying information.
  • FIG. 6 b depicts the abnormality-detailing window 400 of FIG. 6 a , with three additional characteristics that describe the ROI.
  • the Mass 402 is characterized as “Irregular” 420 , “Microlobulated” 422 , and having a “High density” 424 .
  • the addition of the “5 Highly suggestive” 426 attribute indicates that a follow-up examination of the patient is necessary.
  • the radiologist has selected the “Ultrasound guided bx” option 428 , indicating that the recommended next step for the patient is an ultrasound-guided biopsy of the abnormality.
  • FIG. 6 c depicts another example of a right breast MRI abnormality-detailing window 440 and an example of an MRI abnormality-dimensioning window 442 .
  • These two windows display the BI-RADS compatible data points, optionally generated by a CAD software package used to pre-evaluate and generate the ROI in the MIS.
  • the CAD software package can populate the various fields presented by an abnormality window, such as exemplary MRI abnormality-dimensioning window 442 .
  • These widows also provide a radiologist with an interface to adjust, re-characterize, correct, or remove the ROI data based on their professional assessment of the ROI depicted in the patient's images.
  • a radiologist can quickly select or change the radial size, anti-radial size, transverse size, AP size, cranio size, distance from the nipple, distance from the skin, and distance from the chest, of the abnormality. Other appropriate measurements or mechanisms for entering these values are also contemplated.
  • the system contemplated in the example embodiment dynamically updates the values shown in the profiler button 410 , of FIG. 6 b , every time a new attribute is selected in abnormality-detailing window 400 .
  • One embodiment can achieve this high access speed by assigning an enhanced version of ACR lexicon descriptors to individual bits in a group of integers. This approach also yields a relatively compact database size, further minimizing search time.
  • the tables below provide an exemplary sampling of potential abnormality lexicons. Each item in a lexicon is assigned a value.
  • the STATS_VALUES field first provides a specified index into a list of database field values. These database fields are assigned indexes numbered 0 to n-1.
  • the second hexadecimal value is the actual value assigned to the individual lexicon item.
  • the specified bit value is set in the assigned integer field using a bitwise OR operation.
  • the LISTBOX_NAME column provides the general description of where on the abnormality-detailing window 440 the attribute would be grouped.
  • the ITEM_NAME column provides the detailed characteristic that a radiologist can select when characterizing a patent image.
  • the database of ROIs created from all examinations, detailed abnormalities, and pathology is generated and electronically stored at one or more sites. The information is then concatenated. As each exam and abnormality's result is created using the bitwise technique mentioned above, a search is made for an identical pathology finding with the identical set of bitset integer values (lexicon items) describing the abnormalities. If not found, a single record is created for each final abnormality pathology finding for each unique set of integer “lexicon” values. When duplicates are found, abnormality, benign, and malignant, the appropriate counters are incremented and the data displayed in profiler button 410 is updated.
  • the user selects lexicon items and/or pathology findings and the statistical system will instantly show “quick” statistics (total #'s only) in profiler button 410 for other exam abnormalities that “include” the profile of selected items.
  • “quick” statistics total #'s only
  • profiler button 410 for other exam abnormalities that “include” the profile of selected items.
  • the radiologist can also view a statistical list of findings for all abnormalities with “round shape,” perhaps helping determine probabilities for malignancy. If the radiologist subsequently also selects “spiculated margin,” the same process will occur for all abnormalities with a “round shape” AND a “speculated margin.”
  • An example embodiment can use a bit-setting method to produce a typical database that is small enough such that it can be loaded into the main memory of the MIS to enable rapid retrieval and updates.
  • the loading process is performed by a background thread during system startup allowing the user to continue working during loading.
  • all the system needs to do is convert the currently selected lexicon items into their corresponding bitmap values, and then search the database using an “exclusive OR” (xor) comparison on the database records.
  • a record matches when all the “set” bit values from the selected items are “set” in the database record being compared. Abnormality, Benign, and Malignant counts on each matching record are tabulated and ultimately presented to the radiologist.
  • the combination of the high-speed statistical comparison database and the ROI image database allows an embodiment of the system to provide a radiologist with images stored at a local facility for comparative diagnostic purposes.
  • the system also allows a radiologist to select images based on the BI-RADS or other lexicon abnormality descriptors, allowing a comparison of additional images from a larger database or final pathology results if the abnormality was biopsied.
  • Table 2 provides on exemplary mapping of BI-RADS values to the more efficiently stored and searched bit-field values.
  • Detailing window 400 displays information that can be stored as BI-RADS compatible data points, or another suitable lexicon.
  • the ROI data can be generated by a CAD software package used to pre-evaluate and categorize the ROI in the MIS.
  • Detailing window 400 also provides a radiologist with an interface to adjust, re-characterize, correct, or remove the ROI data based on their professional assessment of the ROI depicted in the patient's images if they radiologist disagrees with the CAD generated results. All of this information can be stored in a database configured to correlate all of a patent's ROI data and images.
  • CAD computer aided diagnostic
  • a CAD algorithm may be used to further assist or confirm radiologist assessments of mammography images from new patients, or to alert the medical staff or radiologists when new or previously unclassified abnormalities are detected.
  • the integration of a CAD algorithm and the lexicon abnormality descriptors to generate ROI entries can pre-select the ROI classifications for each abnormality detected by a CAD product. This combination is especially advantageous as it reduces the number of radiologist provided entries to only corrections to the CAD interpretation of an ROI or any ROI that were not categorized initially by the CAD product. While a handful of mouse clicks or keyboard entries, or similar gestures, may seem trivial, the combined time savings over the high volume of patient images that must be reviewed can yield a substantial savings in time, cost and comfort.
  • the display of the statistical results in profiler button 410 is automatically updated every time the radiologist enters or changes a data point.
  • the statistical results display window or profiler button 410 is hidden, or the update suppressed, until the entry of all of the patient's data is complete. This alternative embodiment may be useful as a training tool for educating new radiologists by preventing them from being influenced by the statistical updates as they perform their entry of the data points for a patient.
  • window 500 is displayed.
  • This window of matching statistical information 500 includes the individual quantity 502 and the percentages 504 for malignant and benign outcomes in a sorted itemized list with both local and national data based on the matching selected abnormality features. Additionally, window 500 also includes the various pathology findings 506 , as well as the code for that finding 508 , contained in the database.
  • the example embodiment provides a “show exams” button 510 that allows a radiologist to retrieve the examinations for an individually selected pathology type 512 .
  • FIG. 8 depicts an examination list window 550 for the selected pathological type 512 .
  • the matching exams displayed in FIG. 8 are only those database records from the local facility database. Any records retrieved from a non-local database would not contain any patient identifying information.
  • the embodiment of the MIS depicted here further provides the radiologist with the opportunity to select a record 560 of individual patient with the same diagnosis 512 for further review.
  • the selection of the “View patient priors” button 570 directs the system to open a window containing the selected patient's examination record and “Send Images to Viewstation” button 572 that can be selected to send images to display workstation 100 or image monitors 112 and 114 that allows the radiologist to view multiple matching imaging features and pathological outcomes in similar imaging modalities.
  • FIG. 9 depicts an exemplary prior exam window 600 displaying the images for an individual patient's exam.
  • Prior exam window 600 includes existing or historical exam images for the selected patient for referencing process of care.
  • the radiologist can examine the selected exam report 602 and optionally compare the images contained in that record to the current patient's images.
  • the system allows the radiologist to export a variety of bulk data, such as to a CD or other location with the “Create CD” button 612 option.
  • the bulk data may include all of the images related to a single patient or a collection of categorized abnormality images that match a set of selected abnormality attributes or some other data subset.
  • FIG. 10 depicts a patient report 700 summarizing the details of the CAD or radiologist findings from the examination and analysis of the patient's images.
  • the report 700 can contain a clipped portion of the medical image or a thumbnail picture summarizing the ROI, as well as a multi-perspective wireframe guide that maps the location of the ROI onto the outline of the patient's anatomy.
  • FIG. 11 through FIG. 14 depict an exemplary embodiment of a standalone or web-based interface 800 to an embodiment of the profiler system.
  • the web-based interface 800 can be accessed with any of the commonly available web browsers such as Microsoft Internet Explorer or Mozilla Firefox.
  • a web-based interface may be hosted on a server connected to the Internet for use by a variety of geographically separated individuals or locally where access is limited to a particular facility's local network.
  • FIG. 11 depicts a web-based interface 800 providing a mechanism to select various characteristics regarding abnormality information contained in a database.
  • Four modalities are presented, Mammogram-Mass 802 , Mammogram-Calcification 804 , MRI 806 and Ultrasound (US) 808 .
  • additional characteristics related to the selected modality are displayed to provide further details of the abnormality information request.
  • the example depicted in FIG. 11 indicates a request for abnormality information contained in the database where the abnormality is categorized as a Mammogram-Mass 802 , has an irregular shape 810 , a speculated margin 812 , and a high density 814 .
  • Mammogram-Mass 802 can also have associated calcification types 818 .
  • the Mammogram-Calcification 804 modality is selected as the primary abnormality, and the Mass column containing the Shape 810 , Margin 812 , Density 814 , and Orientation 816 categories, shown in FIG. 11 , are removed from the interface 800 .
  • Interface 800 can include a results summary display area 820 and a matching pathology display area 840 .
  • the results summary display area 820 in a manner similar to the profiler button 410 of FIG. 6 a , displays a count of matching abnormalities and their pathological outcome that were found in the database, as well as the percentages of the biopsied abnormalities that we either malignant or benign.
  • the matching pathology display area 840 can include a list of findings that can detail the percentages of a pathology diagnosis for abnormalities that were malignant or benign.
  • the display area 840 example includes the result 842 as either malignant or benign, the number of entries 844 in the national database, the percentage 846 that each pathology represents of either the malignant or benign diagnosis, a pathology code 848 and a summary of the finding 850 . Both the results summary display area 820 and the matching pathology display area 840 are updated whenever a new abnormality categorization is selected.
  • FIG. 13 depicts an example embodiment of interface 800 displaying categories that are related to the MRI 806 modality.
  • the percentages of the abnormality diagnosis are calculated as the number of relevant diagnosis from the total number of just the malignant or just the benign results. As shown, the percentages of malignant diagnosis equal 100% and the benign diagnosis equal 100%.
  • FIG. 14 depicts an example embodiment of interface 800 displaying categories that are related to the ultrasound 808 modality.
  • the ultrasound 808 modality includes fields for “Boundary,” “Hilum,” Echo,” and “Internal Echo” in column 860 that are specific to ultrasound imaging techniques. It is contemplated that other fields, columns, or modalities can be added or presented as needed to accommodate the preferences of the user or to incorporate other new or existing diagnostic technologies.
  • FIG. 15 depicts an embodiment of a ROI Gallery 900 containing selected image clippings 910 that have been associated with the ROI depicted by the craniocaudal mark 252 .
  • the activation of the “Roi Gallery” button 290 shown in FIG. 3 , causes the ROI Gallery 900 to be presented to the user.
  • the image clippings 910 can be selected from any region of a medical image available to the radiologist on the MIS.
  • a low magnification image 912 can be useful to identify a large area of tissue.
  • a smaller, higher magnification image 914 can provide the radiologist with greater detail.
  • the association of image clippings 910 can allow the radiologist to associate a variety of images with the set of categories, such as those associated with the ROI of FIG. 3 . By correlating a subset of a full resolution image the radiologist is able to focus on the specific area that is described by the characteristics. This correlation of ROI characteristics with any of a variety of radiologist selected image clippings 910 can then be used in during future examinations to quickly focus in on individual areas that may need review.
  • One example would be clipping a view of an abnormality that the radiologist recommended be reviewed after six or twelve months for any changes in size or appearance.
  • the system provides for the clipping of various modalities of images.
  • additional images such as ultrasound or MRI captures can also be included in the gallery.
  • One embodiment of this system can employ the storage of individual image clippings 910 in a compressed image format, such as the JPEG image format established by the Joint Photographic Experts Group, or another appropriate standard.
  • a compressed image format provides an acceptable resolution for a thumbnail image for an initial investigation, while requiring less storage space than a high-resolution image format, such as the DICOM format.
  • the system also provides a link from the compressed image clippings 910 to the full-sized high-resolution image for the situations, such as making a diagnostic assessment, that require a radiologist to view the high-resolution image.
  • a database of thumbnail or clipped images can provide a source of investigational data that may assist a radiologist in categorizing an abnormality that he or she is unfamiliar with, or for use as a training tool.
  • the association of the ROI categorizations with the clipped images also provides an efficient mechanism to search for individual image clippings 910 of a particular type of abnormality or to provide a convenient link to pathology reports or patient correspondence.
  • Non-image based information such as patient correspondence or reports can be stored in the ROI Gallery 500 either in their native format or in an image format, such as JPEG, TIFF, GIF, or another appropriate standard, derived from a screen-capture of the report or document.
  • FIG. 16 a is another depiction of ROI Gallery 900 .
  • Image clipping 910 can be attached or associated directly to an abnormality, such as ROI, depicted by the craniocaudal mark 252 .
  • FIG. 16 b depicts of ROI Gallery 900 with a single highlighted image clipping 910 as indicated by highlight-bar 920 .
  • Various exemplary tools are shown in ROI Gallery 900 that provide for the manipulation of individual image clippings.
  • the title bar 920 changes color, indicating a direct association.
  • Tapping the “+” 924 provides a mechanism to attach image to abnormality 910 .
  • Tapping “ ⁇ ” 926 disassociates image clipping 910 if attached to an ROI.
  • a double-click on image clipping 910 or tapping on magnification button 928 brings up an individual ROI viewer 950 to allow a large view along with access to other imaging tools.
  • the description of the view is displayed from the image it was obtained from, for example RCC (RightCranioCaudal) image.
  • the feature descriptors such as CAD-generated ROI outlines provided by that tool, are displayed.
  • feature descriptors can be superimposed as an overlay on top of the image.
  • a hovering tool bar tool for example when a user leaves the mouse cursor over an image, provides a small message describing the area.
  • the imaging gallery does not allow right ROI to be associated to left breast abnormality, and a left ROI is not allowed to be associated with a right breast image or abnormality.
  • a user can delete 922 the image clipping 910 , or open the image clipping 910 in an individual ROI viewer upon the selection of magnification button 928 .
  • FIG. 17 depicts an example embodiment of a ROI viewer 950 depicting an individual image 952 .
  • the ROI viewer 950 provides additional image manipulation tools, including an “invert” selector 954 that replaces the black pixels for white and the white pixels for black.
  • the ROI viewer 950 also provides a “3D” button 956 that can support the activation of a separate 3D-modeling software package, one example of which is available from Clario, that enables the radiologist to view and rotate a composite three-dimensional image of the associated ROI.
  • the radiologist may return to the ROI Gallery 900 by selecting either the “Exit” button 958 or the “Close Window” icon 960 .
  • FIG. 18 is an example of a patient work-list form 1000 for use with embodiments of this invention.
  • the work-list form 1000 allows the system to coordinate the retrieval of any high-resolution images in order to effectively utilize network bandwidth and system storage capacity.
  • FIG. 19 is an example of a prior examinations form 1100 for use with embodiments of this invention.
  • the prior examination form 1100 provides a radiologist with convenient access to a patient's prior medical image for review or comparison with a more current set of images.
  • the invention provides an integrated system and methods for the categorization, storage, retrieval, and correlation of a wide variety of patient data, diagnostic images from multiple imaging sources, test results, statistics and correspondence.
  • the integration of a ROI profiler, a statistical analysis tool, and the gallery of clipped images, together with native high-resolution medical images provides radiologists and other medical professionals with a customizable tool that provides greater efficiencies while also improving the accuracy of patient diagnostic screenings.

Abstract

A method and system for analyzing and retrieving breast tissue abnormality tracking data, providing a tool for a radiologist that includes a report summarizing the statistical frequency of diagnosed patients, both locally and nationally, with breast tissue region-of-interest classifications similar to the breast tissue images taken of the anatomy of an individual patient. A computer aided diagnostic program can be tested or verified against the breast tissue images and the region-of-interest classifications that have been validated by definitive patient diagnosis. Methods and systems allow the efficient collection of all of the breast tissue abnormalities for a given medical facility in order to provide trending data or radiologist performance analysis. The region-of-interest abnormalities in a single location in a patient's tissue are correlated across a variety of imaging modalities including X-rays, mammogram, CT, ultrasound, MRI, or other imaging technologies.

Description

    RELATED APPLICATIONS
  • This application is a continuation of application Ser. No. 12/625,898 filed Nov. 25, 2009, which claims the benefit of U.S. Provisional Application No. 61/282,000 filed Nov. 24, 2009, which is hereby fully incorporated herein by reference.
  • The following co-pending patent applications of common assignee contain some common disclosure: “Multiple Modality Mammography Image Gallery and Clipping System,” Attorney Docket No. 3080.20US01 and “Mammography Statistical Diagnostic Profiler and Prediction System,” Attorney Docket No. 3080.21US01, filed Nov. 25, 2009, having Ser. No. 12/625,926 (now U.S. Pat. No. 9,171,130) and Ser. No. 12/625,910 (now U.S. Pat. No. 8,687,860), respectively, which are incorporated herein by reference in their entireties.
  • TECHNICAL FIELD
  • The invention relates to management of medical data and more specifically to patient data and breast tissue images originating from multiple modalities.
  • BACKGROUND
  • Historically, interpretation and diagnosis of mammograms and other medical image analysis has been performed using hardcopy x-ray films viewed on an alternator that typically allows x-ray films to be illuminated and masked for diagnostic viewing. Newer technology allows a radiologist or other medical professional to view mammograms and other diagnostic images electronically on high-resolution monitors. These images can also be digitally stored and transmitted across secure networks for archiving or review by other professionals.
  • A radiologist generally begins his or her review process by reviewing a patient's background information relevant to a radiology study, such as a patient's name, age, and any applicable medical conditions or risk factors. After reviewing the background information, the radiologist views multiple images created by radiological, X-ray, computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), tomosynthesis, or other imaging technique of the patient's breast, or other organ, and dictates or uses a computerized information system to track findings, create reports, and make recommendations for future examinations. Such findings can include information pertaining to tissue density, the presence of masses, cysts, calcifications and other abnormalities, or any other breast tissue characteristics.
  • While there has been recent debate regarding the frequency at which women should undergo regular mammogram screenings, and at what age such screenings should begin, it is unlikely that the relatively quick and typically effective practice of mammography screening for breast cancer will disappear completely. Accordingly, there will continue to be a need for radiologists to view and interpret the images generated from patient examinations and screenings. Because the risk of breast cancer threatens the lives of many women, especially those over age 40, radiologists are often inundated with large numbers of mammogram images that must be viewed and, if abnormalities are present, categorized in order to determine if further examination is required. The developments in advanced patient imaging techniques, such as MRI, are also increasing the raw number of images that a radiologist can review. Therefore, there is an ongoing need to improve the speed and efficiency of the radiologist's review of the mammogram images, without sacrificing accuracy, and with the smallest number of false-positive diagnoses. Additionally, given that mammogram screenings are performed periodically, such as annually or biannually, once screening begins for a particular woman, there is also a need to manage, track and analyze data taken over a period of years or decades for that woman.
  • One example of a computerized mammography information system (MIS) to review patient images is the PenRad Mammography Information System available from PenRad. This system provides for the digital presentation of patient data.
  • Legislation has mandated that mammography facilities track positive mammography findings and correlate such findings with biopsy results, maintain statistics for mammography medical outcome and analysis audits on each physician, and provide direct written notification to all patients of their exam results. The generation and correlation of this data is maintained locally by each medical center for each patient.
  • One system for categorizing this information is the Breast Imaging-Reporting and Data System (BI-RADS) published by the American College of Radiology (ACR). BI-RADS provides a system of mammography assessment categories in the form of standardized codes assigned by a radiologist during or after the viewing and interpretation of a medical image. BI-RADS allows for concise and unambiguous understanding of patient records between multiple radiologists and medical facilities. Consequently, a large number of mammogram images, biopsy results, and diagnosis statistics are potentially available in a patient-anonymous format, in compliance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA).
  • Recently, Digital Imaging and Communications in Medicine (DICOM) systems have become the accepted format for medical imaging systems. This format provides for the distribution and viewing of medical studies and images across a variety of platforms. The use of DICOM has, among other things, enabled industry compatibility and improved workflow efficiency between imaging and other information systems located in various healthcare environments. Currently, the DICOM standard is an 18-part publication, PS 3.1-2008 through PS 3.18-2008 describing a standard for digital imaging and communications in medicine developed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA), which is hereby incorporated by reference in its entirety. Among other elements, the DICOM standard provides a method of uniquely numbering any image or other information object to facilitate the unambiguous identification of images or information objects as they are viewed or manipulated in a system or transported across a network.
  • Conventional imaging systems enable a DICOM server to provide medical images across a network to various DICOM compatible clients on the network. Some examples of DICOM clients include picture archiving and communications systems, softcopy workstations, computer-aided diagnosis (CAD) systems, DICOM compatible CD or DVD burners, and other network system devices known to those skilled in the art. One example of a standards-based medical imaging environment is disclosed in U.S. Pat. No. 6,909,795, to Tecotzky et al., incorporated herein by reference.
  • SUMMARY OF THE INVENTION
  • Embodiments of the invention relate to systems and methods of retrieving and analyzing patient data in a mammography information system as part of or in conjunction with the diagnosis and interpretation of patient mammography images that substantially meet the aforementioned needs of the industry. In an example embodiment, the system is capable of retrieving, presenting, and analyzing patent images originating from a variety of modalities.
  • In an embodiment, a configurable mammography diagnostic system comprises a plurality of electronic displays, at least one of the plurality of electronic displays configured to display a breast tissue image having at least one region of interest, a database including a plurality of existing categorizations of at least one known region of interest in at least one of a plurality of breast tissue images, a graphical user interface presented on at least one of the plurality of electronic displays and including an anatomical diagram on which the at least one region of interest can be marked, a detailing button linked to a screen configured to present a plurality of possible characteristics according to which a manual current categorization of a region of interest in the breast tissue image can be defined, and a profiler display button configured to present statistical information related to a comparison of the manual current categorization with the existing categorizations, a clipping tool with which a portion of the breast tissue image displayed on at least one of the plurality of electronic displays can be selected as a second image, the second image displayable on at least one of the plurality of electronic displays as a subset of the breast tissue image, and a processing engine configured to link the second image to the breast tissue image, store the second image in an image database, and to associate the second image with a corresponding region of interest marked on the anatomical diagram.
  • In an embodiment, a method for managing patient mammography data comprises obtaining a plurality of breast tissue images selected from the group consisting of an X-ray image, a CT image, an MRI image, an ultrasound image, and a pathology image, identifying a region of interest in at least one of the plurality of breast tissue images, obtaining a categorization of the region of interest according to an established lexicon, comparing the categorization with a database of existing categorizations and presenting a diagnostic indicator based on the comparing, storing a selected region of the at least one of the plurality of breast tissue images as a second image, mapping the second image to a storage location of the at least one of the plurality of breast tissue images, and associating the selected region with the categorized region of interest.
  • In an embodiment, a mammography information system comprises at least one electronic display, a graphical user interface presented on the at least one electronic display and configured to present data and information related to a patient, the graphical user interface comprising an image gallery configured to display thumbnail representations of a plurality of images that form a portion of the data and information, the plurality of images selectable from X-ray images, CT images, MRI images, ultrasound images, pathology images and document images, and a database operable to store the thumbnail representations of the plurality of images.
  • The above summary of the invention is not intended to describe each illustrated embodiment or every implementation of the present invention. The figures and the detailed description that follow more particularly exemplify these embodiments.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The embodiments of the present invention may be more completely understood in consideration of the following detailed description of various embodiments in connection with the accompanying drawings, in which:
  • FIG. 1 is an example mammogram information system (MIS) display workstation according an embodiment of the invention.
  • FIG. 2 is an example of a mammography exam data-form suitable for use with embodiments of the invention.
  • FIG. 3 is an example of the mammography exam data-form of FIG. 2 indicating a region of interest (ROI).
  • FIG. 4 is an example of a mammogram image with an ROI indicated.
  • FIG. 5 is an example of an ultrasound image with an ROI indicated.
  • FIG. 6 a is another example embodiment of a ROI data entry form for use with embodiments of the invention.
  • FIG. 6 b is the ROI data entry form of FIG. 6 a with additional ROI categorizations entered.
  • FIG. 6 c depicts two additional exemplary embodiments of ROI data entry forms for use with embodiments of the invention.
  • FIG. 7 is an example of a form showing the statistical analysis of a ROI.
  • FIG. 8 is an example of a form showing available images that match statistical analysis of the ROI of FIG. 7.
  • FIG. 9 is an example of a form showing a patient's exam history.
  • FIG. 10 is an example embodiment of a report generated according an embodiment of the invention.
  • FIG. 11 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 12 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 13 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 14 is an example embodiment of a web-based form for use with an embodiment of the invention.
  • FIG. 15 is an example of a mammography exam data-form and an example of a ROI gallery window.
  • FIG. 16 a is an another depiction of the ROI gallery window of FIG. 15.
  • FIG. 16 b is another example of a ROI gallery window for use with embodiments of this invention.
  • FIG. 17 is an example embodiment of a ROI viewer depicting an individual image for use with embodiments of this invention.
  • FIG. 18 is an example of an interpretation work-list form for use with embodiments of the invention.
  • FIG. 19 is an example of a prior examinations form for use with embodiments of this invention.
  • While the present invention is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the intention is not to limit the present invention to the particular embodiments described. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.
  • DETAILED DESCRIPTION OF THE DRAWINGS
  • Embodiments provide a computerized mammography information system that allows for the digital correlation of a wide variety patent data related to a mammography image or other breast tissue diagnostic imaging procedures. An exemplary system is able to electronically track breast tissue abnormalities across multiple image types, provide a customizable interface for convenient and efficient image review, allow for an individual user to save preferred image hanging protocols, categorize multiple imaging types, generate statistics, and provide patient correspondence. Additionally, the integration of various computer aided diagnostic/detection (CAD) protocols for multiple image modalities into the system assists the medical professional in reviewing and accurately diagnosing any abnormalities present in a patient's diagnostic images. This advancement in accuracy also provides the benefit of reducing the need for expensive and invasive biopsy or surgery due to false positive diagnosis.
  • Embodiments of the mammography information system provide an efficient, easy to use, and customizable interface for use by a medical professional for the review and analysis of medical images from a variety of source. The system is capable of integrating medical images acquired through X-ray, CT, ultrasound, MRI, tomosynthesis, or other imaging techniques.
  • The increasing availability and quantity of digital information representing patient medical data and diagnostic images has created a need for a system that allows a doctor or radiologist to quickly review, organize, and if necessary retrieve, multiple diagnostic images that may be indicative of an individual patient's condition. In addition to the availability of digital mammography images, other patient associated data, such as biopsy or other test results and even entire medical histories or correspondence records can be stored in a digital format. Access to images where the pictured abnormality has been definitively diagnosed can assist with the doctor or radiologist's diagnosis of the new patient's individual condition. Prior to the electronic production, archival, and detailed categorization of patient images, such comparisons were limited to a handful of common abnormalities described in the various medical texts or required laborious manual review of individual patient files.
  • Therefore, there is a need for a system that will quickly allow a radiologist to select a ROI in a mammogram or other image and correlate the ROI to a mapping or outline of the patient's anatomy in order to improve efficiency of patient diagnosis and record retrieval including a mechanism to “clip” a ROI from any image modality, or form of electronic record, and associating that “clipping” with a specific ROI placement in the patient's record.
  • Additionally, the availability of this collection of breast tissue images and their associated biopsy results presents an opportunity for statistical analysis of the likelihood that a matching region of interest (ROI) in an individual patient's mammography images is malignant or benign and whether or not a biopsy or further imaging should be ordered. Therefore, there is a need for a system that will quickly allow a radiologist to classify a ROI in a mammogram or other image and correlate the ROI to a large pool of existing data samples that have been definitively diagnosed in order to improve the accuracy and efficiency of patient diagnosis. The radiologist can be assisted in the classification of the ROI by a CAD module by automatically detecting potential ROI abnormalities or simply reducing the number of physical or verbal actions needed by the radiologist to enter the ROI classifying data.
  • In an example embodiment, a MIS is provided for use by a radiologist or other medical professional that preloads all of an individual patient's medical images for a specific portion of the patient's anatomy, regardless of the modality used to create the images. For example, in a breast cancer screening, any available x-ray, ultrasound, MRI, biopsy, or other images for the patient are retrieved and preprocessed by an appropriate CAD algorithm. A CAD module for the appropriate image type can isolate one or more ROI for review in an individual image. The disclosed invention takes these individual CAD results and correlates any common ROI findings between images of the same or different modalities. A summary “map” or outline of the examined patient's anatomy is then generated and displayed for the medical professional along with any other details about the potential ROI(s) that were generated by the CAD module(s).
  • The mammography image gallery and clipping system according to the present invention provides a convenient organization of all of the images associated with a ROI, regardless of modality, for presentation to a medical professional. The system stores lower resolution clippings, or thumbnail images, for pathological images, reports, and abnormalities found, and optionally categorized, by radiologists or CAD products at a facility that have been entered into a mammography information system. The system stores low resolution images as well as the reference to the original image and ROI of the original image. As more patients are definitively diagnosed and their pathology records updated in the system, the larger the collection of abnormality images depicting a previously diagnosed and imaged condition that become available in the system. This system can be integrated into an existing MIS or utilized as a standalone interface providing access to a large sample of mammogram abnormality images.
  • The system also provides an efficient mechanism for creating a comprehensive collection of abnormality data. The collection comprising a uniform lexicon of classifications that allows for further analysis and study of the data while still maintaining patient privacy as required by the applicable law. Those skilled in the art of developing and maintaining electronic databases will appreciate and understand the tradeoffs associated with the storage requirements necessary for the implementation of the contemplated system. As numerous mammography facilities implement this non-patient identifying (and HIPAA compliant) data can be transferred to a central location accumulating a more complete database of abnormality images and the corresponding characterization of data points for various pathology types.
  • In an example embodiment, the method of analyzing and retrieving abnormality tracking data provides a report of the statistical frequency of diagnosed patients both locally and nationally with mammogram ROI classifications similar to an individual patient. The abnormality data can include information disclosing the frequency of similar ROI classifications have been biopsied and the number of biopsies that were malignant or benign. The disclosed method of capturing and reporting abnormality tracking data provides a radiologist or other medical professional a tool to assess the likelihood of a ROI being malignant or benign, and whether or not the patient should undergo additional testing. The system then presents these statistics to the radiologist who can then choose to look further into the underlying related data if he or she desires.
  • The statistical mammography predictive system according to the present invention provides instantly and continually updated outcome statistics to a medical professional. The system utilizes the information and data points for each and every abnormality found by radiologists at a facility that have been entered into a mammography information system. As more patients are definitively diagnosed and their pathology records updated in the system, the greater the chances that an individual patient will have a condition similar to a previously diagnosed and imaged condition. This system can be integrated into an existing MIS or utilized as a standalone interface providing access to a large sample of mammogram abnormality data.
  • The system also provides an efficient mechanism for creating a comprehensive collection of abnormality data. The collection comprising a uniform lexicon of classifications that allows for further analysis and study of the data while still maintaining patient privacy as required by the applicable law. Only unique copies of each combination of tracing data points must be kept in the system. As duplicate data is accumulated the counters of the abnormality and its diagnosis as benign or malignant are incremented. This aggregation of data creates a compact and anonymous abnormality database for the medical location. If desired, a complete reference of all abnormality data can be maintained. Those skilled in the art of developing and maintaining electronic databases will appreciate and understand the tradeoffs associated with the storage requirements necessary for the implementation of the contemplated system.
  • As numerous mammography facilities implement this non-patient identifying (and HIPAA compliant) data can be transferred to a central location accumulating an more complete database of abnormalities and the corresponding benign or malignant counters for each combination of tracking points and pathology. Therefore, the large number of recorded abnormalities can be culled down to a manageable set of unique combinations specified by radiologists around the country. This culling, or grouping of duplicate abnormalities, allows for a medical professional to access a comprehensive database of the known set of abnormalities nearly instantaneously.
  • In a further embodiment, the system disclosed provides a mechanism to evaluate, validate, and improve any of a variety of existing CAD modules and techniques by providing an efficient platform for testing the cad module or technique against a wide variety of known, physician evaluated, and definitively diagnosed, patient abnormalities or ROI.
  • The invention can be better understood by reference to FIGS. 1-19. FIG. 1 illustrates an example embodiment of a mammogram display workstation 100. A typical mammogram display workstation 100 includes a controller display system 110 and at least one high-resolution image monitor 112. One or more additional high-resolution image monitor units 114 can also be used to provide additional viewing area to provide for the comparison of two or more images at full resolution. The controller display system 110 is any of a variety of commonly available video display monitors coupled to a personal computer such as an IBM-PC or compatible system running a version of the Microsoft WINDOWS operating system, or the equivalent thereof. In an embodiment, the image monitors 112 and 114 are liquid crystal displays (LCDs) that provide high-resolution and enhanced contrast for ease of viewing images, but may also be a cathode ray tube or other appropriate display in other embodiments. An exemplary image monitor can display approximately 2500×2000 pixels, although a variety of image monitor sizes are contemplated. In one embodiment, the mammogram display workstation 100 includes a server computer (not shown) that runs DICOM communications components of the mammogram display workstation 100; alternatively, this DICOM software may run on the controller display system 110. In yet another embodiment, a server computer is included that runs an Archived Image Retrieval service; alternatively, this software may also run on the controller display system 110 or on the DICOM compliant server.
  • The mammogram display workstation 100 includes software that allows images to be analyzed using the image processor in the controller display system 110 to analyze each image of a study set, compare with complementary images to generate a suspect list to reduce false indicators, and to generate graphic overlay images to identify areas of interest. When an image is displayed on an image monitor 112 or 114, imaging tools included in the system allow a user working with the system to further manipulate an image. These software tools may provide magnification of a desired region of an image; image inversion, reversal, rotation, or other repositioning; image/background color inversion; noise filtering from images to reduce or eliminate extraneous data and enhance pertinent image data; customized side-by-side image comparisons; and image reorganization, for example.
  • FIG. 2 illustrates an example embodiment of a medical diagnostic system that includes an abnormality-summary window 200. Abnormality-summary window 200 provides a convenient patient information summary 210 and an interface to import or enter additional data. In window 200 the radiologist can enter abnormality data for either the left or right breast by clicking on an “Add Abnormality” button 220. Additionally, a user can import a CAD report detailing any abnormalities that have been detected by existing CAD software. Examples of suitable CAD software include the CadStream product by Confirma and the B-CAD product by Medipattern, among others.
  • As shown in FIG. 3, imported CAD information stored in compliance with a pre-determined system such as BI-RADS can be used to generate a wire-frame map or guide 230 depicting the location and depth of a ROI in or on a patient's anatomy that was detected by the CAD software or entered manually by a radiologist. The density of the patient's tissue is also presented in selector 240. The guide 230 includes both a craniocaudal (CC) view 250 and a mediolateral/oblique (ML) view 260 of both the left and right breasts of a patient. The ROI is depicted by the craniocaudal mark 252 and the mediolateral mark 262. In other situations, an abnormality may only be visible in one or the other of the ML or the CC view and, accordingly, only a single mark would be displayed in either the craniocaudal (CC) view 250 or the mediolateral/oblique (ML) view 260.
  • In an embodiment, the ROI data underlying either craniocaudal mark 252 or mediolateral mark 262 can be represented as the number of pixel spaces from at least two edges of the original image represented by the ROI. The retention of the number of pixels from at least two edges provides for the derivation of the location of the ROI on the original image. This allows the storage of multiple ROI for a single high-resolution image without the need to store multiple copies of the high-resolution image or even high-resolution clippings. It also permits derivation or mapping of an ROI in one image to other images based on known pixel sizes and edge distances.
  • In another alternative embodiment, the data underlying these two marks are used to then calculate an approximate location of the abnormality as viewed by a physician when facing the patient. This calculation also compensates for the fact that during the creation of a mammography image, the patient's breast is compressed to increase the amount of viewable tissue in the two-dimensional x-ray image. Additionally, compensation must be made for the angle at which the mediolateral/oblique view 260 is taken relative to the craniocaudal view 250 during mammogram imaging. Those skilled in the art will appreciate that the two views are not necessarily created at angles exactly perpendicular to each other due to the wide variety of patient anatomy and the need to capture as much tissue as possible in each image. The breast orientation, size and thickness information is provided along with the mammogram image. The resulting combination of the craniocaudal data and the mediolateral data produce the clock-position 270 as shown for the exemplary ROI. This calculation is not calculated if the ROI is only visible on a single image, as both a craniocaudal and mediolateral position are required, along with a distance either from the patient's nipple or chest wall, to calculate the location of the ROI in three-dimensional space.
  • An abnormality does not need to be located or seen in both views to be characterized. Often in mammography an abnormality is only seen in one view and additional imaging is conducted to confirm its location in another view. The additional imaging can also reveal superimposed tissue, a situation in which the breast tissue of several layers was compressed together causing a potential mass seen in a single image with the appearance of an actual abnormality. A radiologist viewing multiple images of the same tissue area can appropriately categorize these situations.
  • Also shown in FIG. 3 is a three-word indication 272 of the approximate location of the ROI in the patient's breast. In this example the ROI is located in the inferior (lower), lateral (outside), middle (distance between the chest and nipple) portion of the patient's right breast. Similar terms for the remaining quadrants and depth are provided by the ACR guidelines and will be understood by those skilled in the art.
  • An additional feature of the system is the capability of importing any ROI from a patient's previous examination that are already present in the system's database. A radiologist or technician can select the “Clone Prev” button 280 to review and import data from a previous examination. This feature further eliminates the need for duplicated effort on the part of the medical professional conducting the review of the patient's exam images.
  • The system is capable of handling a variety of imaging technologies. FIG. 4 depicts an exemplary x-ray generated mammogram image 300 with an ROI indicated by a dashed outline 310 on the image 300 of the patient's breast tissue 320. FIG. 5 depicts an exemplary ultrasound image 330 with an ROI indicated by a dashed outline 340 on the image 330 of the patient's breast tissue 350. While the type of information depicted in a mammogram image 300 is clearly different from the ultrasound image 330, the system maintains the ROI indicated on each respective image by storing the coordinates of each ROI as an offset, in one embodiment utilizing the number of pixels, from at least two edges of the original digital image, regardless of the technique employed to generate the image. These coordinates are then used to calculate the distance from the patient's chest wall, nipple, or other appropriate reference point, to determine the measurements defining the location of the ROI. Similar techniques can be applied to other imaging technologies such as MRI or CT images that are capable of being stored in a standardized digital format where the correlation of the number of pixels in the image to the real-world distance depicted in the image is known.
  • FIG. 6 a depicts an embodiment of an abnormality-detailing window 400. The detailing window 400 provides an interface for a radiologist to enter or view the detailed attributes that describe an abnormality in a selected ROI. FIG. 6 a depicts the single attribute 402 of a “Mass” as being selected to describe the ROI depicted in FIG. 3. As indicated by the system, the presence of a mass alone is generally not enough to indicate the presence of a malignancy. The radiologist then selects an impression 404 and an appropriate recommendation in the “Impression & Recs” area 406. In one embodiment, the system suggests an impression or recommendation in area 406 based on other selected attributes in window 400, which can then be reviewed by the radiologist and altered, if desired. The system can also dynamically and automatically adjust the selection in area 406 if other attributes in window 400 are changed during review. In other embodiments, area 406 is selectable by a radiologist or doctor.
  • The abnormality-detailing window 400 can include a profiler button 410 that provides a count of matching abnormalities and their pathological outcome. The profiler button 410, or another appropriate window, displays the number of biopsies performed that were diagnosed as malignancies 412, the number of biopsies performed that were diagnosed as benign 414, and the total number of matching abnormalities 416 in the database. The sum of the number of malignancies 412 and the number of benign 414 is the total number of biopsies performed on abnormalities possessing the same attributes selected in detailing window 400 at that location. The second line 418 of profiler window 410 displays these same quantities found in a national database. As discussed above, the single attribute of a Mass 402 in FIG. 6 a yields a relatively low number of malignancies 412 (roughly 1.4%) of similar abnormalities in the local database. The combination of the number of malignancies 412 and the number of benign 414 is also a low percentage of the total number of similar abnormalities, indicating a low frequency of requests by the patient's physician for a biopsy. The profiler button 410 is depicted in the lower corner of the screen to provide a convenient, yet out of the way area to present statistical information. Other locations or embodiments, such as a floating window that can be repositioned by the radiologist are contemplated.
  • Two database versions are typically present in every system—one is the “local” version containing the data specific to the medical facility where the system is installed. This local data can be subsequently uploaded to a centralized server to be integrated with into a “regional,” “national,” or “global” version of the database. This allows individual users to compare their own facility's results with a larger sample of results. Additionally, the “local” version can be linked to the on-site examination image data, allowing the radiologist to see other examinations related to a specific pathology finding or set of characteristics. The radiologist can then nearly instantly view selected examinations, images, or specified regions of interest retrieved from the local database. The system can also be configured to link to information and retrieve images from the larger databases, although in one embodiment this can be done without any patient identifying information.
  • FIG. 6 b depicts the abnormality-detailing window 400 of FIG. 6 a, with three additional characteristics that describe the ROI. The Mass 402 is characterized as “Irregular” 420, “Microlobulated” 422, and having a “High density” 424. In the “Impression & Recs” area 406 the addition of the “5 Highly suggestive” 426 attribute indicates that a follow-up examination of the patient is necessary. In this case, the radiologist has selected the “Ultrasound guided bx” option 428, indicating that the recommended next step for the patient is an ultrasound-guided biopsy of the abnormality.
  • The addition of the three ROI characteristics in FIG. 6 b significantly narrowed the number of matching abnormalities in the MIS database as shown in the profiler button 410. While only half of the biopsied abnormalities resulted in a result of malignancy 412 for the local database, as seen in the national database line 418, the vast majority of biopsied abnormalities of this type were malignant. While the relatively low number of data points presented for this abnormality type may not be sufficient to draw any definitive conclusions, this example shows the utility of being able to compare a local sample with a larger multi-site database of abnormalities providing an indication to the local medical personnel that further review of this abnormality scenario may be required. Those skilled in the medical and radiology arts will appreciate these and other advantages that this collection of data and the ease of access provided by the system yield.
  • FIG. 6 c depicts another example of a right breast MRI abnormality-detailing window 440 and an example of an MRI abnormality-dimensioning window 442. These two windows display the BI-RADS compatible data points, optionally generated by a CAD software package used to pre-evaluate and generate the ROI in the MIS. In one embodiment, the CAD software package can populate the various fields presented by an abnormality window, such as exemplary MRI abnormality-dimensioning window 442. These widows also provide a radiologist with an interface to adjust, re-characterize, correct, or remove the ROI data based on their professional assessment of the ROI depicted in the patient's images. As depicted, in abnormality-dimensions window 442 a radiologist can quickly select or change the radial size, anti-radial size, transverse size, AP size, cranio size, distance from the nipple, distance from the skin, and distance from the chest, of the abnormality. Other appropriate measurements or mechanisms for entering these values are also contemplated.
  • The system contemplated in the example embodiment dynamically updates the values shown in the profiler button 410, of FIG. 6 b, every time a new attribute is selected in abnormality-detailing window 400. One embodiment can achieve this high access speed by assigning an enhanced version of ACR lexicon descriptors to individual bits in a group of integers. This approach also yields a relatively compact database size, further minimizing search time. The tables below provide an exemplary sampling of potential abnormality lexicons. Each item in a lexicon is assigned a value. In Table 1, the STATS_VALUES field first provides a specified index into a list of database field values. These database fields are assigned indexes numbered 0 to n-1. The second hexadecimal value is the actual value assigned to the individual lexicon item. When this item is selected during an examination, the specified bit value is set in the assigned integer field using a bitwise OR operation. The LISTBOX_NAME column provides the general description of where on the abnormality-detailing window 440 the attribute would be grouped. The ITEM_NAME column provides the detailed characteristic that a radiologist can select when characterizing a patent image.
  • TABLE 1
    Mammogram Lexicon Item Detailing
    LISTBOX_NAME ITEM_NAME STATS_VALUES
    Specify Abnormality Fibrocystic tissue 0, 0x00000001
    Specify Abnormality Cyst simple 0, 0x00000002
    Specify Abnormality Mastitis area 0, 0x00000004
    Specify Abnormality Mass solid 0, 0x00000008
    Specify Abnormality Lesion 0, 0x00000010
    Specify Abnormality Cyst 0, 0x00000020
    Specify Abnormality Abscess 0, 0x00000040
    Specify Abnormality Mass 0, 0x00000080
    Specify Abnormality Papillary lesion 0, x000000100
    Profile Abnormality Irregular 1, 0x00000001
    Profile Abnormality Lobulated 1, 0x00000002
    Profile Abnormality Oval 1, 0x00000004
    Profile Abnormality Reniform 1, 0x00000008
    Profile Abnormality Round 1, 0x00000010
    Profile Abnormality Circumscribed 1, 0x00000020
    Profile Abnormality Microlobulated 1, 0x00000040
    Profile Abnormality Obscured 1, 0x00000080
    Profile Abnormality Indistinct 1, 0x00000100
    Profile Abnormality Spiculated 1, 0x00000200
    Profile Abnormality Intraductal 1, 0x00000400
    Profile Abnormality Irregular 1, 0x00000800
    Profile Abnormality Smooth 1, 0x00001000
    Profile Abnormality High density 1, 0x00002000
    Profile Abnormality Equal density 1, 0x00004000
    Size and Distance Parallel/skin 1, 0x00800000
    Size and Distance Perpendic/skin 1, 0x01000000
    Assoc Calcs Generic calcs 2, 0x00000001
    Assoc Calcs Amorphous 2, 0x00000002
    Assoc Calcs Branching 2, 0x00000004
    Assoc Calcs Coarse 2, 0x00000008
    Assoc Calcs Dystrophic 2, 0x00000010
    Assoc Calcs Eggshell 2, 0x00000020
    Assoc Calcs Lucent-centered 2, 0x00002000
    Assoc Calcs Milk of calcium 2, 0x00004000
    Assoc Calcs Pleomorphic 2, 0x00008000
    Assoc Calcs Punctate 2, 0x00010000
    Assoc Calcs Rim 2, 0x00020000
    Assoc Calcs Round 2, 0x00040000
    Assoc Calcs Skin 2, 0x00080000
    Assoc Calcs Spherical 2, 0x00100000
    Assoc Calcs Suture 2, 0x00200000
    Assoc Calcs Vascular 2, 0x00400000
    Assoc Calcs Clustered 2, 0x00800000
    Assoc Calcs Diffuse 2, 0x01000000
    Assoc Calcs Grouped 2, 0x02000000
    Assoc Calcs Linear 2, 0x04000000
    Assoc Calcs Regional 2, 0x08000000
    Assoc Calcs Scattered 2, 0x10000000
    Assoc Calcs Segmental 2, 0x20000000
    Associated findings Hematoma 3, 0x00000001
    Associated findings Nipple retract 3, 0x00000002
    Associated findings Seroma 3, 0x00000008
    Associated findings Skin involvement 3, 0x00000010
    Associated findings Skin lesion 3, 0x00000020
    Associated findings Skin retraction 3, 0x00000040
    Associated findings Skin thicken 3, 0x00000080
    Associated findings Trab thicken 3, 0x00000100
    Change From Prior Incr in size 3, 0x00000200
    Change From Prior Decr in size 3, 0x00000400
    Change From Prior Incr in calcs 3, 0x00002000
    Change From Prior Decr in calcs 3, 0x00004000
    Change From Prior Incr in number 3, 0x00008000
    Change From Prior Decr in number 3, 0x00010000
    Change From Prior Less prom. 3, 0x00020000
    Change From Prior More prom. 3, 0x00040000
    Associated findings Archit distortion 3, 0x00080000
    Associated findings Axillary adenop 3, 0x00100000
    Associated findings Chest wall invas 3, 0x00200000
  • The database of ROIs created from all examinations, detailed abnormalities, and pathology is generated and electronically stored at one or more sites. The information is then concatenated. As each exam and abnormality's result is created using the bitwise technique mentioned above, a search is made for an identical pathology finding with the identical set of bitset integer values (lexicon items) describing the abnormalities. If not found, a single record is created for each final abnormality pathology finding for each unique set of integer “lexicon” values. When duplicates are found, abnormality, benign, and malignant, the appropriate counters are incremented and the data displayed in profiler button 410 is updated.
  • In querying the database, the user selects lexicon items and/or pathology findings and the statistical system will instantly show “quick” statistics (total #'s only) in profiler button 410 for other exam abnormalities that “include” the profile of selected items. When the radiologist selects “round shape” he will instantly see statistics for all other abnormalities with a “round shape,” noting how many were ultimately benign, how many were malignant, and how many were never biopsied. The radiologist can also view a statistical list of findings for all abnormalities with “round shape,” perhaps helping determine probabilities for malignancy. If the radiologist subsequently also selects “spiculated margin,” the same process will occur for all abnormalities with a “round shape” AND a “speculated margin.”
  • An example embodiment can use a bit-setting method to produce a typical database that is small enough such that it can be loaded into the main memory of the MIS to enable rapid retrieval and updates. In an embodiment, the loading process is performed by a background thread during system startup allowing the user to continue working during loading. In querying the database, all the system needs to do is convert the currently selected lexicon items into their corresponding bitmap values, and then search the database using an “exclusive OR” (xor) comparison on the database records. A record matches when all the “set” bit values from the selected items are “set” in the database record being compared. Abnormality, Benign, and Malignant counts on each matching record are tabulated and ultimately presented to the radiologist.
  • The combination of the high-speed statistical comparison database and the ROI image database allows an embodiment of the system to provide a radiologist with images stored at a local facility for comparative diagnostic purposes. The system also allows a radiologist to select images based on the BI-RADS or other lexicon abnormality descriptors, allowing a comparison of additional images from a larger database or final pathology results if the abnormality was biopsied. Table 2 provides on exemplary mapping of BI-RADS values to the more efficiently stored and searched bit-field values.
  • TABLE 2
    Mammogram Lexicon to BIRADS Conversion and Detailing
    DATABASE
    DESCRIPTOR BIT-FIELD
    ABNORMALITY CLASSIFICATION ID NUMBER VALUE
    Mass Irregular 16 0x00000001
    Shape Lobulated 190 0x00000002
    Oval 15 0x00000004
    Reniform 27 0x00000008
    Round 14 0x00000010
    Margin Circumscribed 109 0x00000020
    Microlobulated 111 0x00000040
    Obscured 28 0x00000080
    Indistinct 21 0x00000100
    Spiculated 29 0x00000200
    Intraductal 201 0x00000400
    Irregular 20 0x00000800
    Smooth 18 0x00001000
    Density High density 211 0x00002000
    Equal density 213 0x00004000
    Low density 212 0x00008000
    Fat containing 214 0x00010000
    Cent lucent 215 0x00020000
    Wall Septated internal wall 25 0x00080000
    Irregular internal wall 24 0x00100000
    Smooth internal wall 23 0x00200000
    Thickened wall 199 0x00400000
    Calcification (generic calcs) 701 0x00000001
    Type Amorphous 702 0x00000002
    Branching 703 0x00000004
    Coarse 704 0x00000008
    Dystrophic 705 0x00000010
    Eggshell 706 0x00000020
    Fine 707 0x00000040
    Heterogeneous 708 0x00000100
    Indistinct 709 0x00000200
    Large rodlike 710 0x00000400
    Layering 711 0x00000800
    Linear 712 0x00001000
    Lucent-centered 713 0x00002000
    Milk of calcium 714 0x00004000
    Pleomorphic 715 0x00008000
    Punctate 716 0x00010000
    Rim 717 0x00020000
    Round 718 0x00040000
    Skin 719 0x00080000
    Spherical 720 0x00100000
    Suture 721 0x00200000
    Vascular 722 0x00400000
    Calcification Clustered 751 0x00800000
    Distribution Diffuse 752 0x01000000
    Grouped 753 0x02000000
    Linear 754 0x04000000
    Regional 755 0x08000000
    Scattered 756 0x10000000
    Segmental 757 0x20000000
    Foreign body, Hematoma 478 0x00000001
    Scar, or other Nipple retract 477 0x00000002
    (typically ignore) Post surgical scar 479 0x00000004
    Seroma 469 0x00000008
    Skin involvement 252 0x00000010
    Skin lesion 473 0x00000020
    Skin retraction 251 0x00000040
    Skin thicken 250 0x00000080
    Trab thicken 470 0x00000100
    Changes from Incr in size 77 0x00000200
    prior exam Decr in size 78 0x00000400
    Incr in calcs 483 0x00002000
    Decr in calcs 484 0x00004000
    Incr in number (mass) 481 0x00008000
    Decr in number (mass) 482 0x00010000
    Less prom. 293 0x00020000
    More prom. 294 0x00040000
  • Detailing window 400 displays information that can be stored as BI-RADS compatible data points, or another suitable lexicon. Optionally the ROI data can be generated by a CAD software package used to pre-evaluate and categorize the ROI in the MIS. Detailing window 400 also provides a radiologist with an interface to adjust, re-characterize, correct, or remove the ROI data based on their professional assessment of the ROI depicted in the patient's images if they radiologist disagrees with the CAD generated results. All of this information can be stored in a database configured to correlate all of a patent's ROI data and images.
  • The features provided by the system can also be combined with any one of several available computer aided diagnostic (CAD) products to validate, improve, and allow simplified testing of future CAD algorithms. A CAD product can be evaluated by using the electronically compiled descriptions of any abnormalities shown in a collection of ROI images to compare the CAD software algorithms against the real world pathology or biopsy results that were actually performed on the ROIs depicted in the image database.
  • Once the reliable performance of a CAD algorithm is established it may be used to further assist or confirm radiologist assessments of mammography images from new patients, or to alert the medical staff or radiologists when new or previously unclassified abnormalities are detected. Additionally, the integration of a CAD algorithm and the lexicon abnormality descriptors to generate ROI entries, such as those depicted in FIG. 6 b, can pre-select the ROI classifications for each abnormality detected by a CAD product. This combination is especially advantageous as it reduces the number of radiologist provided entries to only corrections to the CAD interpretation of an ROI or any ROI that were not categorized initially by the CAD product. While a handful of mouse clicks or keyboard entries, or similar gestures, may seem trivial, the combined time savings over the high volume of patient images that must be reviewed can yield a substantial savings in time, cost and comfort.
  • In the example embodiment discussed above, the display of the statistical results in profiler button 410 is automatically updated every time the radiologist enters or changes a data point. In another embodiment, the statistical results display window or profiler button 410 is hidden, or the update suppressed, until the entry of all of the patient's data is complete. This alternative embodiment may be useful as a training tool for educating new radiologists by preventing them from being influenced by the statistical updates as they perform their entry of the data points for a patient.
  • As shown in FIG. 7, when the user activates, or clicks on, the profiler button 410 of FIG. 6 b, a window of matching statistical information 500 is displayed. This window of matching statistical information 500 includes the individual quantity 502 and the percentages 504 for malignant and benign outcomes in a sorted itemized list with both local and national data based on the matching selected abnormality features. Additionally, window 500 also includes the various pathology findings 506, as well as the code for that finding 508, contained in the database.
  • The example embodiment provides a “show exams” button 510 that allows a radiologist to retrieve the examinations for an individually selected pathology type 512. FIG. 8 depicts an examination list window 550 for the selected pathological type 512. The matching exams displayed in FIG. 8 are only those database records from the local facility database. Any records retrieved from a non-local database would not contain any patient identifying information. The embodiment of the MIS depicted here further provides the radiologist with the opportunity to select a record 560 of individual patient with the same diagnosis 512 for further review. The selection of the “View patient priors” button 570 directs the system to open a window containing the selected patient's examination record and “Send Images to Viewstation” button 572 that can be selected to send images to display workstation 100 or image monitors 112 and 114 that allows the radiologist to view multiple matching imaging features and pathological outcomes in similar imaging modalities.
  • FIG. 9 depicts an exemplary prior exam window 600 displaying the images for an individual patient's exam. Prior exam window 600 includes existing or historical exam images for the selected patient for referencing process of care. By selecting an individual exam report 602 and then one of the “View Full” 604, “Preview” 606, “Print” 608, or “Send to Viewstation” 610, the radiologist can examine the selected exam report 602 and optionally compare the images contained in that record to the current patient's images. Additionally, the system allows the radiologist to export a variety of bulk data, such as to a CD or other location with the “Create CD” button 612 option. The bulk data may include all of the images related to a single patient or a collection of categorized abnormality images that match a set of selected abnormality attributes or some other data subset.
  • FIG. 10 depicts a patient report 700 summarizing the details of the CAD or radiologist findings from the examination and analysis of the patient's images. The report 700 can contain a clipped portion of the medical image or a thumbnail picture summarizing the ROI, as well as a multi-perspective wireframe guide that maps the location of the ROI onto the outline of the patient's anatomy.
  • FIG. 11 through FIG. 14 depict an exemplary embodiment of a standalone or web-based interface 800 to an embodiment of the profiler system. The web-based interface 800 can be accessed with any of the commonly available web browsers such as Microsoft Internet Explorer or Mozilla Firefox. As appreciated by those skilled in the art, a web-based interface may be hosted on a server connected to the Internet for use by a variety of geographically separated individuals or locally where access is limited to a particular facility's local network.
  • FIG. 11 depicts a web-based interface 800 providing a mechanism to select various characteristics regarding abnormality information contained in a database. Four modalities are presented, Mammogram-Mass 802, Mammogram-Calcification 804, MRI 806 and Ultrasound (US) 808. Depending on the modality selected, additional characteristics related to the selected modality are displayed to provide further details of the abnormality information request. The example depicted in FIG. 11 indicates a request for abnormality information contained in the database where the abnormality is categorized as a Mammogram-Mass 802, has an irregular shape 810, a speculated margin 812, and a high density 814. Mammogram-Mass 802 can also have associated calcification types 818.
  • As depicted in FIG. 12, the Mammogram-Calcification 804 modality is selected as the primary abnormality, and the Mass column containing the Shape 810, Margin 812, Density 814, and Orientation 816 categories, shown in FIG. 11, are removed from the interface 800. Interface 800 can include a results summary display area 820 and a matching pathology display area 840. The results summary display area 820, in a manner similar to the profiler button 410 of FIG. 6 a, displays a count of matching abnormalities and their pathological outcome that were found in the database, as well as the percentages of the biopsied abnormalities that we either malignant or benign.
  • The matching pathology display area 840 can include a list of findings that can detail the percentages of a pathology diagnosis for abnormalities that were malignant or benign. The display area 840 example includes the result 842 as either malignant or benign, the number of entries 844 in the national database, the percentage 846 that each pathology represents of either the malignant or benign diagnosis, a pathology code 848 and a summary of the finding 850. Both the results summary display area 820 and the matching pathology display area 840 are updated whenever a new abnormality categorization is selected.
  • FIG. 13 depicts an example embodiment of interface 800 displaying categories that are related to the MRI 806 modality. As shown in the “Percent of” column 852 of the matching pathology display area 840, the percentages of the abnormality diagnosis are calculated as the number of relevant diagnosis from the total number of just the malignant or just the benign results. As shown, the percentages of malignant diagnosis equal 100% and the benign diagnosis equal 100%.
  • FIG. 14 depicts an example embodiment of interface 800 displaying categories that are related to the ultrasound 808 modality. The ultrasound 808 modality includes fields for “Boundary,” “Hilum,” Echo,” and “Internal Echo” in column 860 that are specific to ultrasound imaging techniques. It is contemplated that other fields, columns, or modalities can be added or presented as needed to accommodate the preferences of the user or to incorporate other new or existing diagnostic technologies.
  • FIG. 15 depicts an embodiment of a ROI Gallery 900 containing selected image clippings 910 that have been associated with the ROI depicted by the craniocaudal mark 252. The activation of the “Roi Gallery” button 290, shown in FIG. 3, causes the ROI Gallery 900 to be presented to the user. The image clippings 910 can be selected from any region of a medical image available to the radiologist on the MIS. A low magnification image 912 can be useful to identify a large area of tissue. Alternatively, a smaller, higher magnification image 914 can provide the radiologist with greater detail.
  • The association of image clippings 910 can allow the radiologist to associate a variety of images with the set of categories, such as those associated with the ROI of FIG. 3. By correlating a subset of a full resolution image the radiologist is able to focus on the specific area that is described by the characteristics. This correlation of ROI characteristics with any of a variety of radiologist selected image clippings 910 can then be used in during future examinations to quickly focus in on individual areas that may need review. One example would be clipping a view of an abnormality that the radiologist recommended be reviewed after six or twelve months for any changes in size or appearance.
  • Additionally, the system provides for the clipping of various modalities of images. In addition to the mammogram images as shown in the ROI Gallery 900, additional images such as ultrasound or MRI captures can also be included in the gallery. One embodiment of this system can employ the storage of individual image clippings 910 in a compressed image format, such as the JPEG image format established by the Joint Photographic Experts Group, or another appropriate standard. The use of a compressed image format provides an acceptable resolution for a thumbnail image for an initial investigation, while requiring less storage space than a high-resolution image format, such as the DICOM format. The system also provides a link from the compressed image clippings 910 to the full-sized high-resolution image for the situations, such as making a diagnostic assessment, that require a radiologist to view the high-resolution image.
  • In one embodiment of the system, a database of thumbnail or clipped images can provide a source of investigational data that may assist a radiologist in categorizing an abnormality that he or she is unfamiliar with, or for use as a training tool. The association of the ROI categorizations with the clipped images also provides an efficient mechanism to search for individual image clippings 910 of a particular type of abnormality or to provide a convenient link to pathology reports or patient correspondence. Non-image based information such as patient correspondence or reports can be stored in the ROI Gallery 500 either in their native format or in an image format, such as JPEG, TIFF, GIF, or another appropriate standard, derived from a screen-capture of the report or document.
  • FIG. 16 a is another depiction of ROI Gallery 900. Image clipping 910, as well as other images, can be attached or associated directly to an abnormality, such as ROI, depicted by the craniocaudal mark 252. FIG. 16 b depicts of ROI Gallery 900 with a single highlighted image clipping 910 as indicated by highlight-bar 920. Various exemplary tools are shown in ROI Gallery 900 that provide for the manipulation of individual image clippings. When an image is associated to an abnormality, the title bar 920 changes color, indicating a direct association. Tapping the “+” 924 provides a mechanism to attach image to abnormality 910. Tapping “−” 926 disassociates image clipping 910 if attached to an ROI. A double-click on image clipping 910 or tapping on magnification button 928 brings up an individual ROI viewer 950 to allow a large view along with access to other imaging tools.
  • Within the title bar the description of the view is displayed from the image it was obtained from, for example RCC (RightCranioCaudal) image. In an embodiment, if the image was processed through a CAD tool, the feature descriptors, such as CAD-generated ROI outlines provided by that tool, are displayed. In another embodiment, feature descriptors can be superimposed as an overlay on top of the image. Alternatively, a hovering tool bar tool, for example when a user leaves the mouse cursor over an image, provides a small message describing the area. Additionally, in order to reduce right/left errors when associating images to an ROI, the imaging gallery does not allow right ROI to be associated to left breast abnormality, and a left ROI is not allowed to be associated with a right breast image or abnormality.
  • As depicted, a user can delete 922 the image clipping 910, or open the image clipping 910 in an individual ROI viewer upon the selection of magnification button 928.
  • FIG. 17 depicts an example embodiment of a ROI viewer 950 depicting an individual image 952. The ROI viewer 950 provides additional image manipulation tools, including an “invert” selector 954 that replaces the black pixels for white and the white pixels for black. The ROI viewer 950 also provides a “3D” button 956 that can support the activation of a separate 3D-modeling software package, one example of which is available from Clario, that enables the radiologist to view and rotate a composite three-dimensional image of the associated ROI. The radiologist may return to the ROI Gallery 900 by selecting either the “Exit” button 958 or the “Close Window” icon 960.
  • FIG. 18 is an example of a patient work-list form 1000 for use with embodiments of this invention. The work-list form 1000 allows the system to coordinate the retrieval of any high-resolution images in order to effectively utilize network bandwidth and system storage capacity. FIG. 19 is an example of a prior examinations form 1100 for use with embodiments of this invention. The prior examination form 1100 provides a radiologist with convenient access to a patient's prior medical image for review or comparison with a more current set of images.
  • As shown by the preceding examples, the invention provides an integrated system and methods for the categorization, storage, retrieval, and correlation of a wide variety of patient data, diagnostic images from multiple imaging sources, test results, statistics and correspondence. The integration of a ROI profiler, a statistical analysis tool, and the gallery of clipped images, together with native high-resolution medical images provides radiologists and other medical professionals with a customizable tool that provides greater efficiencies while also improving the accuracy of patient diagnostic screenings.
  • The foregoing descriptions present numerous specific details that provide a thorough understanding of various embodiments of the invention. It will be apparent to one skilled in the art that various embodiments, having been disclosed herein, may be practiced without some or all of these specific details. In other instances, known components have not been described in detail in order to avoid unnecessarily obscuring the present invention. It is to be understood that even though numerous characteristics and advantages of various embodiments are set forth in the foregoing description, together with details of the structure and function of various embodiments, this disclosure is illustrative only. Other embodiments may be constructed that nevertheless employ the principles and spirit of the present invention. Accordingly, this application is intended to cover any adaptations or variations of the invention. It is manifestly intended that this invention be limited only by the following claims and equivalents thereof.
  • For purposes of interpreting the claims for the present invention, it is expressly intended that the provisions of Section 112, sixth paragraph of 35 U.S.C. are not to be invoked with respect to a given claim unless the specific terms “means for” or “step for” are recited in that claim.
  • Any incorporation by reference of documents above is limited such that no subject matter is incorporated that is contrary to the explicit disclosure herein. Any incorporation by reference of non-priority documents above is further limited such that no claims included in the documents are incorporated by reference herein and any definitions provided in the documents are not incorporated by reference herein unless expressly included herein.

Claims (16)

1-20. (canceled)
21. A mammography information system comprising:
at least one electronic display;
a graphical user interface presented on the at least one electronic display and configured to present data related to a patient, the graphical user interface comprising an image gallery configured to display thumbnail representations of a plurality of images that form a portion of the data and information, each the plurality of images having an imaging modality of at least one at radiological images, X-ray images, computed tomography images, magnetic resonance images, ultrasound images, pathology images, tomosynthesis images, and document images;
a thumbnail database operable to store the thumbnail representations of the plurality of images;
a first networked database operable to store a plurality of medical-facility-categorized images including categorizations from a medical facility for comparison against the plurality of images; and
a second networked database operable to store a plurality of multiple-medical-facility-categorized images including categorizations from a least two medical facilities for comparison against the plurality of images.
22. The system of claim 21, wherein the document images comprise at least a portion of at least one of a report, a letter, a lab result, and a text document.
23. The system of claim 21, further comprising an anatomical diagram presented on the graphical user interface and on which at least one region of interest corresponding to a region of interest in at least one of the plurality of images can be indicated by a mark.
24. The system of claim 21, wherein the mark on the anatomical diagram is linked to the at least one of the plurality of images.
25. The system of claim 21, wherein the graphical user interface is configured to present an adjacent display of statistical information from a comparison of one of the plurality of images with the categorizations of the first networked database and the second networked database.
26. The system of claim 25, wherein the statistical information comprises a number of biopsies performed that were diagnosed as malignancies, a number of biopsies performed that were diagnosed as benign, and a total number of matching abnormalities.
27. The system of claim 21, wherein each of the plurality of medical-facility-categorized images and each of the plurality of multiple-medical-facility-categorized images is cataloged in the first or second networked databases according to a characteristic of the image.
28. A multiple modality tissue image profiler comprising:
a database of existing pathological findings for a plurality of tissue abnormalities, each tissue abnormality located in a region of interest of one of a plurality of tissue images;
a graphical user interface configured to present a plurality of possible characteristics according to which a tissue abnormality can be characterized; and
a processor configured to identify existing categorizations in the database that match selected ones of the plurality of possible characteristics and to retrieve at least one image having at least one of the selected ones of the plurality of possible characteristics based on a comparison with the existing categorizations and to present the at least one image depicting the characterized tissue abnormality in the graphical user interface,
wherein each of the plurality of images has an imaging modality of at least one of radiological, X-ray, computed tomography, magnetic resonance, ultrasound, pathology, and tomosynthesis.
29. The image profile of claim 28, wherein the existing categorizations in the database are identified using a computer aided diagnosis (CAD) categorization.
30. The image profile of claim 28, wherein the database comprises existing pathological findings for a plurality of tissue abnormalities for a local medical facility.
31. The image profile of claim 30, further comprising a second database of existing pathological findings for a plurality of tissue abnormalities, wherein the second database comprises existing pathological findings for a plurality of medical facilities.
32. The image profiler of claim 31, wherein the processor is further configured to identify existing categorizations in the second database that match selected ones of the plurality of possible characteristics and to retrieve at least one image of the second database having at least one of the selected ones of the plurality of possible characteristics based on a comparison with the existing categorizations and to present the at least one image of the second database depicting the characterized tissue abnormality in the graphical user interface.
33. The image profiler of claim 28, wherein the database is configured to store the existing pathological findings according to a hexadecimal value assigned to at least one of the selected ones of the plurality of possible characteristics.
34. The image profiler of claim 33, wherein the processor is configured to identify the existing categorizations in the database that match selected ones of the plurality of possible characteristics based on a bitwise operation of the hexadecimal value.
35. The image profiler of claim 34, wherein the processor is further configured to identify the existing categorizations in the database that match selected ones of the plurality of possible characteristics based on an index value to the hexadecimal values.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150097868A1 (en) * 2012-03-21 2015-04-09 Koninklijkie Philips N.V. Clinical workstation integrating medical imaging and biopsy data and methods using same
US20150279061A1 (en) * 2014-03-31 2015-10-01 Kabushiki Kaisha Toshiba Medical image processing apparatus and medical image processing system
WO2018057984A1 (en) * 2016-09-23 2018-03-29 Curemetrix, Inc. Mapping of breast arterial calcifications
US10304564B1 (en) * 2017-12-13 2019-05-28 International Business Machines Corporation Methods and systems for displaying an image
JP2021527473A (en) * 2018-06-14 2021-10-14 ケイロン メディカル テクノロジーズ リミテッド Immediate close inspection

Families Citing this family (48)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1986548B1 (en) 2006-02-15 2013-01-02 Hologic, Inc. Breast biopsy and needle localization using tomosynthesis systems
US9286443B2 (en) * 2007-06-04 2016-03-15 Rapid Systems, Llc Systems and methods for data aggregation and prioritization
US8799013B2 (en) * 2009-11-24 2014-08-05 Penrad Technologies, Inc. Mammography information system
US9183355B2 (en) * 2009-11-24 2015-11-10 Penrad Technologies, Inc. Mammography information system
US9451924B2 (en) * 2009-12-30 2016-09-27 General Electric Company Single screen multi-modality imaging displays
US9734285B2 (en) * 2010-05-20 2017-08-15 General Electric Company Anatomy map navigator systems and methods of use
EP2630600A1 (en) * 2010-10-19 2013-08-28 Koninklijke Philips Electronics N.V. System and method for dynamic growing of a patient database with cases demonstrating special characteristics
BR112013011680A2 (en) * 2010-11-10 2016-08-02 Rolando Rafael Hernandez Cisneros method for detection and classification of microcalcifying groups in digital mammograms
US9075903B2 (en) 2010-11-26 2015-07-07 Hologic, Inc. User interface for medical image review workstation
US20120166219A1 (en) * 2010-12-28 2012-06-28 Allscripts Health Care Solutions, Inc. Visual charting method for creating electronic medical documents
DE102011080260B4 (en) * 2011-08-02 2021-07-15 Siemens Healthcare Gmbh Method and arrangement for the computer-aided display and evaluation of medical examination data
GB2495150B (en) * 2011-09-30 2015-07-01 Mirada Medical Method and system of defining a region of interest on medical scan images
CN102521476A (en) * 2011-11-03 2012-06-27 丁国成 Abnormal heart great vessel computer screening system and screening method on basis of adult X-ray normal position chest radiography
KR20130053587A (en) * 2011-11-15 2013-05-24 삼성전자주식회사 Medical device and medical image displaying method using the same
JP2014534042A (en) 2011-11-27 2014-12-18 ホロジック, インコーポレイテッドHologic, Inc. System and method for generating 2D images using mammography and / or tomosynthesis image data
CA2871674A1 (en) * 2012-05-31 2013-12-05 Ikonopedia, Inc. Image based analytical systems and processes
CN104379062B (en) * 2012-06-22 2017-10-31 皇家飞利浦有限公司 Time dissection target indicia in angiogram
JP6081093B2 (en) * 2012-07-09 2017-02-15 東芝メディカルシステムズ株式会社 Image display device
WO2015002256A1 (en) * 2013-07-03 2015-01-08 株式会社東芝 Medical information processing system, medical information processing program, and ultrasound diagnostic device
JP5896084B2 (en) * 2013-08-01 2016-03-30 パナソニック株式会社 Similar case search device, control method of similar case search device, and program
KR20150034061A (en) 2013-09-25 2015-04-02 삼성전자주식회사 The method and apparatus for setting imaging environment by using signals received from a plurality of clients
KR102244258B1 (en) * 2013-10-04 2021-04-27 삼성전자주식회사 Display apparatus and image display method using the same
US20150117731A1 (en) * 2013-10-24 2015-04-30 Jacob Levman Computational metric that forms a component of computer-aided detection systems for magnetic resonance imaging
KR20150074304A (en) * 2013-12-23 2015-07-02 삼성전자주식회사 Method for Providing Medical Image and Apparatus Thereof
EP3868301B1 (en) 2014-02-28 2023-04-05 Hologic, Inc. System and method for generating and displaying tomosynthesis image slabs
WO2015134668A1 (en) * 2014-03-04 2015-09-11 The Regents Of The University Of California Automated quality control of diagnostic radiology
US10586618B2 (en) 2014-05-07 2020-03-10 Lifetrack Medical Systems Private Ltd. Characterizing states of subject
US10340041B2 (en) * 2014-05-09 2019-07-02 Acupath Laboratories, Inc. Biopsy mapping tools
US10595805B2 (en) 2014-06-27 2020-03-24 Sunnybrook Research Institute Systems and methods for generating an imaging biomarker that indicates detectability of conspicuity of lesions in a mammographic image
KR20160012758A (en) * 2014-07-25 2016-02-03 삼성전자주식회사 Apparatus and Method for aiding image diagnosis
KR20160054992A (en) * 2014-11-07 2016-05-17 삼성전자주식회사 Apparatus and method of evading re-detected region of interest
KR102307356B1 (en) 2014-12-11 2021-09-30 삼성전자주식회사 Apparatus and method for computer aided diagnosis
DE102015201361A1 (en) * 2015-01-27 2016-07-28 Siemens Healthcare Gmbh Data system for identifying radiology records
WO2016142492A1 (en) 2015-03-10 2016-09-15 Koninklijke Philips N.V. Retrieval of corresponding structures in pairs of medical images
US10275876B2 (en) 2015-06-12 2019-04-30 International Business Machines Corporation Methods and systems for automatically selecting an implant for a patient
US10489010B1 (en) 2015-07-11 2019-11-26 Allscripts Software, Llc Methodologies involving use of avatar for clinical documentation
KR102354458B1 (en) * 2015-08-19 2022-01-21 삼성전자주식회사 Display apparatus and controlling method thereof
USD822700S1 (en) * 2016-11-18 2018-07-10 General Electric Company Display screen or portion thereof with graphical user interface
JP6849462B2 (en) 2017-02-06 2021-03-24 キヤノンメディカルシステムズ株式会社 Medical information processing system and medical image processing device
US10734100B2 (en) 2017-02-09 2020-08-04 Leavitt Medical, Inc. Systems and methods for tissue sample processing
US11246550B2 (en) * 2017-06-16 2022-02-15 Volpara Health Technologies Limited Method for detection and quantification of arterial calcification
EP3641635A4 (en) 2017-06-20 2021-04-07 Hologic, Inc. Dynamic self-learning medical image method and system
US10832808B2 (en) 2017-12-13 2020-11-10 International Business Machines Corporation Automated selection, arrangement, and processing of key images
US20210137384A1 (en) * 2017-12-13 2021-05-13 Washington University System and method for determining segments for ablation
AU2019349684A1 (en) * 2018-09-24 2021-03-11 Hologic, Inc. Breast mapping and abnormality localization
KR102075293B1 (en) * 2019-05-22 2020-02-07 주식회사 루닛 Apparatus for predicting metadata of medical image and method thereof
CN112381164B (en) * 2020-11-20 2022-09-20 北京航空航天大学杭州创新研究院 Ultrasound image classification method and device based on multi-branch attention mechanism
US20230125385A1 (en) * 2021-10-25 2023-04-27 Hologic, Inc. Auto-focus tool for multimodality image review

Family Cites Families (132)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5021770A (en) * 1987-07-15 1991-06-04 Hitachi, Ltd. Image display system and data input apparatus used therein
US4972264A (en) 1989-06-19 1990-11-20 International Business Machines Corporation Method and apparatus for viewing an overscanned image
US5325478A (en) 1989-09-15 1994-06-28 Emtek Health Care Systems, Inc. Method for displaying information from an information based computer system
US5708810A (en) 1989-10-10 1998-01-13 Unisys Corporation Image-based document processing system having a platform architecture
US5212637A (en) 1989-11-22 1993-05-18 Stereometrix Corporation Method of investigating mammograms for masses and calcifications, and apparatus for practicing such method
US6311419B1 (en) 1989-12-31 2001-11-06 Smartlight Ltd. Dedicated mammogram viewer
US5241659A (en) * 1990-09-14 1993-08-31 Eastman Kodak Company Auxiliary removable memory for storing image parameter data
DE69131681T2 (en) 1990-11-22 2000-06-08 Toshiba Kawasaki Kk Computerized diagnostic system for medical use
US5229585A (en) 1991-02-19 1993-07-20 Minnesota Mining And Manufacturing Company Film cartridge bar code scanner and controller for a digital imaging system
US5343390A (en) * 1992-02-28 1994-08-30 Arch Development Corporation Method and system for automated selection of regions of interest and detection of septal lines in digital chest radiographs
US5321520A (en) 1992-07-20 1994-06-14 Automated Medical Access Corporation Automated high definition/resolution image storage, retrieval and transmission system
US5452416A (en) * 1992-12-30 1995-09-19 Dominator Radiology, Inc. Automated system and a method for organizing, presenting, and manipulating medical images
US6434262B2 (en) 1993-09-29 2002-08-13 Shih-Ping Wang Computer-aided diagnosis system and method
US5670984A (en) * 1993-10-26 1997-09-23 Xerox Corporation Image lens
US5719567A (en) * 1995-05-30 1998-02-17 Victor J. Norris, Jr. System for enhancing navigation and surveillance in low visibility conditions
US5565678A (en) 1995-06-06 1996-10-15 Lumisys, Inc. Radiographic image quality assessment utilizing a stepped calibration target
JPH09181893A (en) * 1995-12-26 1997-07-11 Minolta Co Ltd Facsimile equipment and image communication method
US6006191A (en) 1996-05-13 1999-12-21 Dirienzo; Andrew L. Remote access medical image exchange system and methods of operation therefor
US5917929A (en) 1996-07-23 1999-06-29 R2 Technology, Inc. User interface for computer aided diagnosis system
US6243095B1 (en) 1996-12-05 2001-06-05 Peter E. Shile Navigation and display system for digital radiographs
US6047257A (en) * 1997-03-01 2000-04-04 Agfa-Gevaert Identification of medical images through speech recognition
US6246782B1 (en) 1997-06-06 2001-06-12 Lockheed Martin Corporation System for automated detection of cancerous masses in mammograms
US6317617B1 (en) 1997-07-25 2001-11-13 Arch Development Corporation Method, computer program product, and system for the automated analysis of lesions in magnetic resonance, mammogram and ultrasound images
US6347299B1 (en) * 1997-07-31 2002-02-12 Ncr Corporation System for navigation and editing of electronic records through speech and audio
US6970587B1 (en) 1997-08-28 2005-11-29 Icad, Inc. Use of computer-aided detection system outputs in clinical practice
US7308126B2 (en) 1997-08-28 2007-12-11 Icad, Inc. Use of computer-aided detection system outputs in clinical practice
US6630937B2 (en) 1997-10-30 2003-10-07 University Of South Florida Workstation interface for use in digital mammography and associated methods
US20020070973A1 (en) 1998-03-23 2002-06-13 Croley James E. Method of entering, updating and display of related data on a single screen page
US6347241B2 (en) * 1999-02-02 2002-02-12 Senorx, Inc. Ultrasonic and x-ray detectable biopsy site marker and apparatus for applying it
US20010043742A1 (en) 1998-04-29 2001-11-22 Roger D Melen Communication document detector
US6260021B1 (en) 1998-06-12 2001-07-10 Philips Electronics North America Corporation Computer-based medical image distribution system and method
US6176429B1 (en) 1998-07-17 2001-01-23 Psc Scanning, Inc. Optical reader with selectable processing characteristics for reading data in multiple formats
US6304848B1 (en) * 1998-08-13 2001-10-16 Medical Manager Corp. Medical record forming and storing apparatus and medical record and method related to same
US6349143B1 (en) * 1998-11-25 2002-02-19 Acuson Corporation Method and system for simultaneously displaying diagnostic medical ultrasound image clips
US6253184B1 (en) 1998-12-14 2001-06-26 Jon Ruppert Interactive voice controlled copier apparatus
US6355024B1 (en) 1999-07-14 2002-03-12 Mallinckrodt Inc. Medical fluid delivery system
TW406009B (en) 1999-07-16 2000-09-21 Nat Science Council 3-D localization method of clustered microcalcifications using cranio-caudal and medio-lateral oblique views
US6941323B1 (en) * 1999-08-09 2005-09-06 Almen Laboratories, Inc. System and method for image comparison and retrieval by enhancing, defining, and parameterizing objects in images
US6766297B1 (en) * 1999-12-29 2004-07-20 General Electric Company Method of integrating a picture archiving communication system and a voice dictation or voice recognition system
US6901156B2 (en) 2000-02-04 2005-05-31 Arch Development Corporation Method, system and computer readable medium for an intelligent search workstation for computer assisted interpretation of medical images
WO2001059687A1 (en) 2000-02-09 2001-08-16 Patientpower.Com, Llc Method and system for managing patient medical records
WO2001099094A1 (en) * 2000-06-19 2001-12-27 Shima Seiki Mfg., Ltd. Preview image display method, and preview image display device
US20020016718A1 (en) * 2000-06-22 2002-02-07 Rothschild Peter A. Medical image management system and method
US6678703B2 (en) * 2000-06-22 2004-01-13 Radvault, Inc. Medical image management system and method
WO2002025588A2 (en) * 2000-09-21 2002-03-28 Md Online Inc. Medical image processing systems
US7146031B1 (en) 2000-11-22 2006-12-05 R2 Technology, Inc. Method and system for automatic identification and orientation of medical images
US7940966B2 (en) * 2000-11-24 2011-05-10 U-Systems, Inc. Full-field breast image data processing and archiving
US7556602B2 (en) * 2000-11-24 2009-07-07 U-Systems, Inc. Breast cancer screening with adjunctive ultrasound mammography
US7103205B2 (en) 2000-11-24 2006-09-05 U-Systems, Inc. Breast cancer screening with ultrasound image overlays
US7615008B2 (en) * 2000-11-24 2009-11-10 U-Systems, Inc. Processing and displaying breast ultrasound information
US6831648B2 (en) 2000-11-27 2004-12-14 Silicon Graphics, Inc. Synchronized image display and buffer swapping in a multiple display environment
US7081976B2 (en) * 2000-11-29 2006-07-25 Xerox Corporation Color calibration alarm apparatus and method for use in an image-rendering device
US20040085443A1 (en) * 2000-12-13 2004-05-06 Kallioniemi Olli P Method and system for processing regions of interest for objects comprising biological material
JP3850662B2 (en) * 2000-12-27 2006-11-29 独立行政法人科学技術振興機構 Skin permeation gas collection device
GB0101343D0 (en) * 2001-01-19 2001-03-07 Secr Defence Electro-optic waveguide device
US20020107885A1 (en) 2001-02-01 2002-08-08 Advanced Digital Systems, Inc. System, computer program product, and method for capturing and processing form data
US6629378B2 (en) 2001-03-27 2003-10-07 Gregory Gustafson Controlled light source device for reading x-rays
US20020161628A1 (en) 2001-04-26 2002-10-31 C. Lane Poor Voter feedback and receipt system
US7668718B2 (en) * 2001-07-17 2010-02-23 Custom Speech Usa, Inc. Synchronized pattern recognition source data processed by manual or automatic means for creation of shared speaker-dependent speech user profile
US7386847B2 (en) * 2001-10-01 2008-06-10 International Business Machines Corporation Task roster
US6785358B2 (en) * 2001-10-09 2004-08-31 General Electric Company Voice activated diagnostic imaging control user interface
US20030110178A1 (en) 2001-11-21 2003-06-12 Icad, Inc. Method and system of tracking medical films and associated digital images for computer-aided and diagnostic analysis
US20030103663A1 (en) * 2001-11-23 2003-06-05 University Of Chicago Computerized scheme for distinguishing between benign and malignant nodules in thoracic computed tomography scans by use of similar images
WO2003067371A2 (en) * 2002-02-08 2003-08-14 Giger Maryellen L Method and system for risk-modulated diagnosis of disease
JP3836068B2 (en) * 2002-03-05 2006-10-18 三菱重工業株式会社 Air-floating web running support device and coating device using the same
US7124760B2 (en) 2002-05-16 2006-10-24 Endocare, Inc. Template for the localization of lesions in a breast and method of use thereof
US7523505B2 (en) 2002-08-16 2009-04-21 Hx Technologies, Inc. Methods and systems for managing distributed digital medical data
JP4189726B2 (en) 2002-09-12 2008-12-03 コニカミノルタホールディングス株式会社 Image information processing apparatus, medical network system, and program for image information processing apparatus
US7418119B2 (en) 2002-10-31 2008-08-26 Siemens Computer Aided Diagnosis Ltd. Display for computer-aided evaluation of medical images and for establishing clinical recommendation therefrom
US7203350B2 (en) * 2002-10-31 2007-04-10 Siemens Computer Aided Diagnosis Ltd. Display for computer-aided diagnosis of mammograms
US7616801B2 (en) 2002-11-27 2009-11-10 Hologic, Inc. Image handling and display in x-ray mammography and tomosynthesis
US7583861B2 (en) 2002-11-27 2009-09-01 Teramedica, Inc. Intelligent medical image management system
US20040122702A1 (en) * 2002-12-18 2004-06-24 Sabol John M. Medical data processing system and method
US20040181412A1 (en) * 2003-02-26 2004-09-16 Wido Menhardt Medical imaging analysis using speech synthesis
US6909795B2 (en) 2003-06-16 2005-06-21 R2 Technology, Inc. Communicating computer-aided detection results in a standards-based medical imaging environment
US20040258291A1 (en) 2003-06-20 2004-12-23 Gustafson Gregory A. Method and system for tracking abnormality data
US20040258287A1 (en) 2003-06-23 2004-12-23 Gustafson Gregory A. Method and system for configuring a scanning device without a graphical user interface
JP2007524467A (en) 2003-06-24 2007-08-30 アイキャド・インコーポレーテッド Automated computer-aided diagnostic equipment
WO2005001740A2 (en) * 2003-06-25 2005-01-06 Siemens Medical Solutions Usa, Inc. Systems and methods for automated diagnosis and decision support for breast imaging
EP1643370A4 (en) 2003-07-02 2009-04-01 Satoshi Yamatake Image database system
US20050177312A1 (en) 2003-08-20 2005-08-11 Duke University Real-time medical data recording system and method
US7247139B2 (en) 2003-09-09 2007-07-24 Ge Medical Systems Global Technology Company, Llc Method and apparatus for natural voice control of an ultrasound machine
US20050096530A1 (en) 2003-10-29 2005-05-05 Confirma, Inc. Apparatus and method for customized report viewer
JP2005149181A (en) 2003-11-17 2005-06-09 Konica Minolta Medical & Graphic Inc Medical image information management system
US20050123185A1 (en) * 2003-11-26 2005-06-09 Ram Balasubramanian Computer aided detection workflow and user interface
JP3879732B2 (en) * 2003-11-27 2007-02-14 コニカミノルタホールディングス株式会社 Object detection apparatus, object detection method, and computer program
US7727151B2 (en) 2003-11-28 2010-06-01 U-Systems Inc. Navigation among multiple breast ultrasound volumes
JP2005218796A (en) 2004-02-09 2005-08-18 Matsushita Electric Ind Co Ltd Medical image processor and medical image processing method
WO2005088520A1 (en) * 2004-03-11 2005-09-22 University Of Cincinnati Automated spine survey iterative scan technique (assist)
EP1733327A2 (en) * 2004-03-26 2006-12-20 Siemens Medical Solutions Health Services Corporation A system supporting exchange of medical data and images between different executable applications
US7310651B2 (en) 2004-08-18 2007-12-18 Ashok Dave Medical media file management system and method
US7834891B2 (en) * 2004-11-23 2010-11-16 General Electric Company System and method for perspective-based procedure analysis
US7616793B2 (en) 2004-12-30 2009-11-10 Hologic, Inc. Medical image review workstation with integrated content-based resource retrieval
US8428969B2 (en) 2005-01-19 2013-04-23 Atirix Medical Systems, Inc. System and method for tracking medical imaging quality
KR20080021723A (en) 2005-06-02 2008-03-07 더 메디패턴 코포레이션 System and method of computer-aided detection
US7809175B2 (en) * 2005-07-01 2010-10-05 Hologic, Inc. Displaying and navigating computer-aided detection results on a review workstation
US7889896B2 (en) * 2005-08-18 2011-02-15 Hologic, Inc. Patient worklist management in digital radiography review workstations
US20070098243A1 (en) * 2005-10-28 2007-05-03 Gustafson Gregory A Smart destination image routing system
US7885443B2 (en) * 2005-11-14 2011-02-08 Hologic, Inc. Facilitating temporal comparison of medical images
US20070118399A1 (en) * 2005-11-22 2007-05-24 Avinash Gopal B System and method for integrated learning and understanding of healthcare informatics
US20080255849A9 (en) * 2005-11-22 2008-10-16 Gustafson Gregory A Voice activated mammography information systems
US8014576B2 (en) * 2005-11-23 2011-09-06 The Medipattern Corporation Method and system of computer-aided quantitative and qualitative analysis of medical images
US7752060B2 (en) 2006-02-08 2010-07-06 Health Grades, Inc. Internet system for connecting healthcare providers and patients
US20070211930A1 (en) 2006-03-09 2007-09-13 Terry Dolwick Attribute based image enhancement and display for medical imaging applications
US7945083B2 (en) 2006-05-25 2011-05-17 Carestream Health, Inc. Method for supporting diagnostic workflow from a medical imaging apparatus
JP2008000536A (en) * 2006-06-26 2008-01-10 Fujifilm Corp Image display device
US7840046B2 (en) 2006-06-27 2010-11-23 Siemens Medical Solutions Usa, Inc. System and method for detection of breast masses and calcifications using the tomosynthesis projection and reconstructed images
US8606497B2 (en) 2006-11-03 2013-12-10 Salient Imaging, Inc. Method, system and computer program for detecting and monitoring human activity utilizing location data
US7953613B2 (en) * 2007-01-03 2011-05-31 Gizewski Theodore M Health maintenance system
US8014578B2 (en) 2007-02-05 2011-09-06 General Electric Company Method and system for image segmentation using models
US8126267B2 (en) 2007-02-05 2012-02-28 Albany Medical College Methods and apparatuses for analyzing digital images to automatically select regions of interest thereof
US8296247B2 (en) 2007-03-23 2012-10-23 Three Palm Software Combination machine learning algorithms for computer-aided detection, review and diagnosis
US8340387B2 (en) 2007-04-13 2012-12-25 Three Palm Software Fast preprocessing algorithms for digital mammography CAD and workstation
US10032236B2 (en) 2007-04-26 2018-07-24 General Electric Company Electronic health record timeline and the human figure
US8086002B2 (en) 2007-04-27 2011-12-27 Three Palm Software Algorithms for selecting mass density candidates from digital mammograms
US8208700B2 (en) 2007-05-15 2012-06-26 Three Palm Software Mass spicules detection and tracing from digital mammograms
US20090238422A1 (en) 2008-05-13 2009-09-24 Three Palm Software Communicative cad system for assisting breast imaging diagnosis
US9218656B2 (en) * 2007-10-05 2015-12-22 Siemens Medical Solutions Usa, Inc. Method and system for automatic classification of lesions in breast MRI
US8803911B2 (en) * 2007-11-16 2014-08-12 Three Palm Software User interface and viewing workflow for mammography workstation
US8107700B2 (en) 2007-11-21 2012-01-31 Merge Cad Inc. System and method for efficient workflow in reading medical image data
KR20100096224A (en) * 2007-12-03 2010-09-01 데이타피직스 리서치 인코포레이티드 Systems and methods for efficient imaging
JP2011505966A (en) * 2007-12-11 2011-03-03 エピ‐サイ,リミテッド・ライアビリティ・カンパニー Electrical bioimpedance analysis as a biomarker of breast density and / or breast cancer risk
US20090154782A1 (en) * 2007-12-17 2009-06-18 Three Palm Software Dual-magnify-glass visualization for soft-copy mammography viewing
US20090165009A1 (en) * 2007-12-19 2009-06-25 Three Palm Software Optimal scheduling for cad architecture
US20090238421A1 (en) 2008-03-18 2009-09-24 Three Palm Software Image normalization for computer-aided detection, review and diagnosis
US20110087137A1 (en) * 2008-06-16 2011-04-14 Reed Hanoun Mobile fitness and personal caloric management system
US8547402B2 (en) * 2009-10-07 2013-10-01 Hologic, Inc. Displaying computer-aided detection information with associated breast tomosynthesis image information
US20100158332A1 (en) 2008-12-22 2010-06-24 Dan Rico Method and system of automated detection of lesions in medical images
CA2702319C (en) * 2009-03-11 2011-06-14 Mytrak Health System Inc. Ergonomic/physiotherapy programme monitoring system and method of using same
US9183355B2 (en) 2009-11-24 2015-11-10 Penrad Technologies, Inc. Mammography information system
US8799013B2 (en) * 2009-11-24 2014-08-05 Penrad Technologies, Inc. Mammography information system
US9119559B2 (en) * 2011-06-16 2015-09-01 Salient Imaging, Inc. Method and system of generating a 3D visualization from 2D images

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150097868A1 (en) * 2012-03-21 2015-04-09 Koninklijkie Philips N.V. Clinical workstation integrating medical imaging and biopsy data and methods using same
US9798856B2 (en) * 2012-03-21 2017-10-24 Koninklijke Philips N.V. Clinical workstation integrating medical imaging and biopsy data and methods using same
US20150279061A1 (en) * 2014-03-31 2015-10-01 Kabushiki Kaisha Toshiba Medical image processing apparatus and medical image processing system
US9396534B2 (en) * 2014-03-31 2016-07-19 Toshiba Medical Systems Corporation Medical image processing apparatus and medical image processing system
WO2018057984A1 (en) * 2016-09-23 2018-03-29 Curemetrix, Inc. Mapping of breast arterial calcifications
US10973469B2 (en) 2016-09-23 2021-04-13 Curemetrix, Inc. Mapping of breast arterial calcifications
US10304564B1 (en) * 2017-12-13 2019-05-28 International Business Machines Corporation Methods and systems for displaying an image
JP2021527473A (en) * 2018-06-14 2021-10-14 ケイロン メディカル テクノロジーズ リミテッド Immediate close inspection

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