Hologic, Inc.

R2 Technology, Inc.

Mevis Medical Solutions, Inc.

Iiologic, Inc.

Haili Chui

Xiangwei Zhang

Kevin Kreeger

Nikolaos A Gkanatsios

Julian Marshall

Liyang Wei

Jun Ge

Ashwini Kshirsagar

Jin-Long Chen

Jay A Stein

Andrew P Smith

Christopher Ruth

Georgia Hitzke

Yiheng Zhang

Xiaomin Liu

David Mislan

Mark A Prazer

Zhenxue Jing

Jimmy R Roehrig

Biao Chen

Sussan Pourjavid

Xiaolan Zhang

Akira Hasegawa

Adora Dsouza

Wei Zhang

Chukka Srinivas

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.

System and method for navigating a tomosynthesis stack including automatic focusing

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.

System and method for hierarchical multi-level feature image synthesis and representation

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.

System and method for navigating a tomosynthesis stack using synthesized image data

A method of correlating regions in an image pair including a cranial-caudal image and a medial-lateral-oblique image. Data from a similarity matching model is received by an ensemble model, the data including at least a matched pair of regions and a first confidence level indicator associated with the matched pair of regions. Data from a geo-matching model is received by the ensemble model, the data from the geo-matching model including at least the matched pair of regions and a second confidence level indicator. A joint probability of correlation is determined by the ensemble model based on evaluation of each of the first and second confidence level by the ensemble matching model, wherein the joint probability of correlation provides a probability that the region in each image correlates to the corresponding region in the other image. The joint probability of correlation is provided to an output device.

Systems and methods for correlating objects of interest

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.

Personalized breast imaging system

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.

Image generation by high density element suppression

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.

System and method for synthesizing low-dimensional image data from high-dimensional image data using an object grid enhancement

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.

System and method for generating a 2D image using mammography and/or tomosynthesis image data

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.

AI system for predicting reading time and reading complexity for reviewing 2D/3D breast images

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.

System and method for targeted object enhancement to generate synthetic breast tissue images

System and method for synthetic breast tissue image generation by high density element suppression

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Fremont, CA, United States of America

Haili Chui