US 12,664,656 B2 · Correlating regions of interest

Abstract

Methods and systems for identifying a region of interest in breast tissue use artificial intelligence to confirm that a target lesion identified during initial imaging the breast tissue has been identified in a subsequent imaging session. A computing system operating a lesion matching engine uses a machine learning classifier algorithm trained on cases of initial and subsequent two-dimensional or three-dimensional images of lesions. The lesion matching engine analyzes a target lesion identified with initial and a potential lesion identified with current imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

Claims20

Dependent claims

A method of locating of a lesion within a breast, the method comprising:

receiving, at a computing device, an initial image of a target lesion within the breast, wherein the initial image was generated during an initial imaging session;
receiving, at the computing device, a current image of the breast obtained during a current biopsy session;
receiving, at the computing device, an indication of a location of a potential lesion in the current image;
analyzing, with a machine learning lesion classifier, the potential lesion to compare the potential lesion to the target lesion and determine a level of confidence that the potential lesion corresponds to the target lesion, wherein the machine learning lesion classifier analyzes lesions for correlations in one or more of density, stiffness, shape, size, and texture;
outputting an indicator of the level of confidence for display on a graphical user interface of a biopsy computing device;
and
based at least partially on the level of confidence, navigating a biopsy device, via the biopsy computing device, to the potential lesion during the current biopsy session.

4 more elements

A lesion identification system comprising:

a processing device;
and
a memory storing instructions that, when executed by the processing device, facilitate performance of operations, comprising:
accessing an initial x-ray image of a breast, the initial x-ray image including an identified lesion indicated with a visual marker;
receiving a current x-ray image of the breast, the current x-ray image including an indication of a potential lesion, wherein the current x-ray image is obtained during a current biopsy session;
analyzing the potential lesion and the identified lesion using an artificial intelligence lesion classifier, wherein the artificial intelligence lesion classifier is trained on digital breast tomosynthesis images, and wherein the artificial intelligence lesion classifier analyzes lesions for correlations in one or more of density, stiffness, shape, margins, orientation, texture, pattern, size, and depth within the breast;
generating a confidence score indicating a likelihood that the potential lesion in the current x-ray image matches the identified lesion in the initial x-ray image;
displaying an output associated with the confidence score on a graphical user interface;
and
based at least partially on the confidence score, navigating a biopsy device, via the graphical user interface, towards the potential lesion during the current biopsy session.

7 more elements

Dependencies are inferred from the claim text.

Correlating regions of interest

Life of this patent

Correlating regions of interest

Life of this patent

Abstract

Claims20