Hologic, Inc.

Ashwini Kshirsagar

Haili Chui

Xiangwei Zhang

Jay A Stein

Andrew P Smith

Christopher Ruth

Kevin Kreeger

Liyang Wei

Jun Ge

Yiheng Zhang

Nikolaos A Gkanatsios

Alan W Rego

David Wolff

Adrian Hunsdon

Rachel Chandler

John W Robinson

Adora Dsouza

Kenneth F Defreitas

Kenneth F DeFreitas

John LaViola

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

Methods and systems for imaging. For example, a system may include a breast compression paddle, an imaging detector, at least one sensor incorporated into at least one of the breast compression paddle or the imaging detector. The system performs operations including generating, at a first time point, first spatial data of the breast based on data captured by the at least one sensor; generating, at a second time point, second spatial data of the breast based on data captured by the at least one sensor; based on the first spatial data and the second spatial data, determining an amount of motion of the breast that occurred; and based on the determined amount of motion performing at least one of: generating a motion map for the breast; discarding one or more acquired projections for use in generating a tomosynthesis reconstruction; or correcting a medical image acquired the second time point.

Motion, compression, and positioning corrections in medical imaging

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

A method of imaging a breast of a patient using an imaging system includes applying, with a first component of the imaging system, a compressive force to the breast. A second component of the imaging system is positioned in a start position. The imaging system sends a first guidance signal to the patient. An imaging procedure of the breast is performed with the second component of the imaging system. Subsequent to performing the imaging procedure, a second guidance signal is sent to the patient.

Systems and methods for guiding a patient during imaging procedures

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

Methods, systems and related computer program products are provided for processing a medical image of a breast to detect anatomical abnormalities therein, including anatomical abnormalities that may be associated with breast cancer. The medical image of the breast, which includes a background region bordering a breast tissue region along a skinline thereof, is processed to detect an inward-facing retraction along the skinline, which can be potentially indicative of an anatomical abnormality in the breast tissue. In one preferred embodiment, a display monitor displays first information representative of the medical image of the breast and second information identifying a location of the detected inward-facing retraction on the medical image of the breast. In another preferred embodiment, one or more metrics characterizing the detected inward-facing retraction are used as features in the classification of potential CAD detections in the breast tissue region.

Processing medical images of the breast to detect anatomical abnormalities therein

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Cupertino, CA, United States of America

Ashwini Kshirsagar