Abstract: A method for processing breast tissue image data includes processing the image data to generate a set of image slices collectively depicting the patient's breast; for each image slice, applying one or more filters associated with a plurality of multi-level feature modules, each configured to represent and recognize an assigned characteristic or feature of a high-dimensional object; generating at each multi-level feature module a feature map depicting regions of the image slice having the assigned feature; combining the feature maps generated from the plurality of multi-level feature modules into a combined image object map indicating a probability that the high-dimensional object is present at a particular location of the image slice; and creating a 2D synthesized image identifying one or more high-dimensional objects based at least in part on object maps generated for a plurality of image slices.

Abstract: A method and system for acquiring, processing, storing, and displaying x-ray mammograms Mp tomosynthesis images Tr representative of breast slices, and x-ray tomosynthesis projection images Tp taken at different angles to a breast, where the Tr images are reconstructed from Tp images.

Abstract: A method for processing breast tissue image data includes processing the image data to generate a set of image slices collectively depicting the patient's breast; for each image slice, applying one or more filters associated with a plurality of multi-level feature modules, each configured to represent and recognize an assigned characteristic or feature of a high-dimensional object; generating at each multi-level feature module a feature map depicting regions of the image slice having the assigned feature; combining the feature maps generated from the plurality of multi-level feature modules into a combined image object map indicating a probability that the high-dimensional object is present at a particular location of the image slice; and creating a 2D synthesized image identifying one or more high-dimensional objects based at least in part on object maps generated for a plurality of image slices.

Abstract: Methods and systems for performing motion correction on imaging data. The methods include accessing a set of imaging data. The set of imaging data includes first imaging data for a first time point, second imaging data for a second time point, and third imaging data for a third time point. A location of a region of interest (ROI) is identified in the different imaging data. Differences between the locations of ROI across the imaging data are determined and aggregated to generate an aggregate motion score for the respective imaging data in the set of imaging data. One of the imaging data is then selected as reference imaging data for motion correction based on the aggregate motion score. Motion correction of the set of imaging data is performed based on the selected reference imaging data. Similar comparisons on images may be performed for peak enhancement of MRI imaging data.

Abstract: Examples of the present disclosure describe systems and methods for using AI to identify regions of interest (ROI) in medical images. In aspects, medical reports and images may be provided to a second environment. The second environment may use the medical report data/medical images to train a natural language processing (NLP)-based algorithm to identify the location in images of ROI described in the medical report data. The output of the NLP-based algorithm may be stored in an ROI repository in the second environment. After the NLP-based algorithm has been trained, a request to train a user-specific model may be received in a first environment. Data objects for the requested user-specific model may be provided to the second environment, which uses the ROI repository to train the model. The trained model may be provided to the first environment, where the trained user-specific model/algorithm may be tested and stored.

Abstract: Examples of the present disclosure describe systems and methods for using AI to identify regions of interest (ROI) in medical images. In aspects, medical reports and images may be provided to a first environment. The first environment may use the medical report data/medical images to train a natural language processing (NLP)-based algorithm to identify the location in images of ROI described in the medical report data. The output of the NLP-based algorithm may be stored in an ROI repository in the first environment. After the NLP-based algorithm has been trained, a request to train a user-specific model may be received in a second environment. Data objects for the requested user-specific model may be provided to the first environment, which uses the ROI repository to train the model. The trained model may be provided to the second environment, where the trained user-specific model/algorithm may be tested and stored.

Abstract: A method and breast imaging system for processing breast tissue image data includes feeding image data of breast images to an image processor, identifying image portions depicting breast tissue and high density elements and executing different processing methods on input images. A first image processing method involves breast tissue enhancement and high density element suppression, whereas the second image processing method involves enhancing high density elements. Respective three-dimensional sets of image slices may be generated by respective image processing methods, and respective two-dimensional synthesized images are generated and combined to form a two-dimensional composite synthesized image which is presented through a display of the breast imaging system. First and second image processing may be executed on generated three-dimensional image sets or two-dimensional projection images acquired by an image acquisition component at respective angles relative to the patient's breast.

Abstract: Systems and methods for generating and using a customized hanging protocol for the display of medical images. The methods may include receiving an indication to create a first hanging step of the hanging protocol, displaying a workspace having a plurality of viewports for displaying medical images, and displaying a plurality of building blocks corresponding to different types of medical images. The method may also include receiving a selection of a first building block in the plurality of building blocks, wherein the first building block corresponds to a first type of medical image, and receiving an indication of a location in the workspace for the first building block to be placed. Based on the indication of the location in the workspace for the first building block, one or more of the plurality of viewports is filled with the first building block.

Abstract: A system and method for displaying and navigating breast tissue is configured for or includes obtaining a plurality of 2D and/or 3D images of a patient's breast; generating a synthesized 2D image of the breast from the obtained images; displaying the synthesized 2D image; receiving a user command, or otherwise detecting through a user interface, a user selection or other indication of an object or region in the synthesized 2D image; and displaying at least a portion of one or more images from the plurality, including a source image and/or most similar representation of the user selected or indicated object or region.

Abstract: A method for processing breast tissue image data includes processing the image data to generate a set of image slices collectively depicting the patient's breast; for each image slice, applying one or more filters associated with a plurality of multi-level feature modules, each configured to represent and recognize an assigned characteristic or feature of a high-dimensional object; generating at each multi-level feature module a feature map depicting regions of the image slice having the assigned feature; combining the feature maps generated from the plurality of multi-level feature modules into a combined image object map indicating a probability that the high-dimensional object is present at a particular location of the image slice; and creating a 2D synthesized image identifying one or more high-dimensional objects based at least in part on object maps generated for a plurality of image slices.

Abstract: A system and method for displaying and navigating breast tissue is configured for or includes obtaining a plurality of 2D and/or 3D images of a patient's breast; generating a synthesized 2D image of the breast from the obtained images; displaying the synthesized 2D image; receiving a user command, or otherwise detecting through a user interface, a user selection or other indication of an object or region in the synthesized 2D image; and displaying at least a portion of one or more images from the plurality, including a source image and/or most similar representation of the user selected or indicated object or region.

Abstract: A method for processing breast tissue image data includes obtaining image data of a patient's breast tissue, processing the image data to generate a set of image slices, the image slices collectively depicting the patient's breast tissue; feeding image slices of the set through each of a plurality of object-recognizing modules, each of the object-recognizing modules being configured to recognize a respective type of object that may be present in the image slices; combining objects recognized by the respective object-recognizing modules to generate a synthesized image of the patient's breast tissue; and displaying the synthesized image.

Abstract: Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.

Abstract: Examples of the present disclosure describe systems and methods for automating clinical workflow decisions. In aspects, patient data may be collected from multiple data sources, such as patient records, imaging data, etc. The patient data may be processed using an artificial intelligence (AI) component. The output of the AI component may be used by healthcare professionals to inform healthcare decisions for patients. The output of the AI component and additional information relating to the healthcare decisions and healthcare paths may be provided as input to a decision analysis component. The decision analysis component may process the input and output an automated healthcare recommendation that may be used to further inform the healthcare decisions of the healthcare professionals. In some aspects, the output of the decision analysis component may be used to determine a priority or timeline for performing one or more actions relating to patient healthcare.

Abstract: A method and system for acquiring, processing, storing, and displaying x-ray mammograms Mp tomosynthesis images Tr representative of breast slices, and x-ray tomosynthesis projection images Tp taken at different angles to a breast, where the Tr images are reconstructed from Tp images

Abstract: A system for processing breast tissue images includes an image processing computer and a user interface operatively coupled to the image processing computer, wherein the image processing computer is configured to obtain image data of breast tissue, processing the image data to generate a set of reconstructed image slices, the reconstructed image slices collectively depicting the breast tissue, process respective subsets of the reconstructed image slices to generate a set of image slabs, each image slab comprising a synthesized 2D image of a portion of the breast tissue obtained from a respective subset of the set of reconstructed image slices.

Abstract: A method and system for acquiring, processing, storing, and displaying x-ray mammograms Mp tomosynthesis images Tr representative of breast slices, and x-ray tomosynthesis projection images Tp taken at different angles to a breast, where the Tr images are reconstructed from Tp images.