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 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: Examples of the present disclosure describe systems and methods for personalized breast imaging. In aspects, a first set of patient attributes may be collected. The first set of patient attributes may relate to, or be used to determine, for example, breast size, breast thickness, and/or three-dimensional (3D) breast shape. The first set of patient attributes may be used to customize image acquisition parameters for the patient. A second set of patient attributes may also be collected. The second set of patient attributes may relate to, or be used to determine, for example, breast elasticity and breast density. The second set of patient attributes may be used to customize breast compression parameters for the patient. The customized image acquisition parameters and breast compression parameters may then be used to perform one or more procedures (e.g., an imaging procedure, a biopsy procedure, etc.) on the patient's breast.

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: 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: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

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: The invention includes a method including the steps of obtaining a plurality of images, each of the images in the plurality having at least one corresponding region, generating a merged image, the merged image also having the corresponding region. The step of generating includes selecting an image source from the plurality of images to source image data for the corresponding region in the merged image by comparing attributes of the corresponding regions of the plurality of images to identify the image source having preferred attributes.

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 system and method for reviewing a tomosynthesis image data set comprising volumetric image data of a breast, the method comprising, in one embodiment, causing an image or a series of images from the data set to be displayed on a display monitor and selecting or indicating through a user interface an object or region of interest in a presently displayed image of the data set, thereby causing an image from the data set having a best focus measure of the user selected or indicated object or region of interest to be automatically displayed on the display monitor.

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: 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: 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 for processing breast tissue image data includes processing image data of a patient's breast tissue to generate a high-dimensional grid depicting one or more high-dimensional objects in the patient's breast tissue; determining a probability or confidence of each of the one or more high-dimensional objects depicted in the high-dimensional grid; and modifying one or more aspects of at least one of the one or more high-dimensional objects based at least in part on its respective determined probability or confidence to thereby generate a lower-dimensional format version of the one or more high-dimensional objects. The method may further include displaying the lower-dimensional format version of the one or more high-dimensional objects in a synthesized image of the patient's breast tissue.

Abstract: The invention includes a method including the steps of obtaining a plurality of images, each of the images in the plurality having at least one corresponding region, generating a merged image, the merged image also having the corresponding region. The step of generating includes selecting an image source from the plurality of images to source image data for the corresponding region in the merged image by comparing attributes of the corresponding regions of the plurality of images to identify the image source having preferred attributes.

Abstract: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

Abstract: The invention includes a method including the steps of obtaining a plurality of images, each of the images in the plurality having at least one corresponding region, generating a merged image, the merged image also having the corresponding region. The step of generating includes selecting an image source from the plurality of images to source image data for the corresponding region in the merged image by comparing attributes of the corresponding regions of the plurality of images to identify the image source having preferred attributes.

Abstract: A method for processing breast tissue image data includes processing image data of a patient's breast tissue to generate a high-dimensional grid depicting one or more high-dimensional objects in the patient's breast tissue; determining a probability or confidence of each of the one or more high-dimensional objects depicted in the high-dimensional grid; and modifying one or more aspects of at least one of the one or more high-dimensional objects based at least in part on its respective determined probability or confidence to thereby generate a lower-dimensional format version of the one or more high-dimensional objects. The method may further include displaying the lower-dimensional format version of the one or more high-dimensional objects in a synthesized image of the patient's breast tissue.

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.