公开(公告)号：US20230005140A1

公开(公告)日：2023-01-05

申请号：US17899232

申请日：2022-08-30

Applicant: Genentech, Inc.

Inventor： Gregory Zelinsky FERL ,  Richard Alan Duray CARANO ,  Kai Henrik BARCK ,  Jasmine PATIL

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/774

Abstract: Methods and systems disclosed herein relate generally to processing images to estimate whether at least part of a tumor is represented in the images. A computer-implemented method includes accessing an image of at least part of a biological structure of a particular subject, processing the image using a segmentation algorithm to extract a plurality of image objects depicted in the image, determining one or more structural characteristics associated with an image object of the plurality of image objects, processing the one or more structural characteristics using a trained machine-learning model to generate estimation data corresponding to an estimation of whether the image object corresponds to a lesion or tumor associated with the biological structure, and outputting the estimation data for the particular subject.

公开(公告)号：US20220375116A1

公开(公告)日：2022-11-24

申请号：US17850474

申请日：2022-06-27

Applicant: Genentech, Inc.

Inventor： Jasmine PATIL ,  Alexander James Stephen CHAMPION DE CRESPIGNY ,  Richard Alan Duray CARANO

IPC: G06T7/62 ,  G06T7/30 ,  G06T7/70 ,  G06T7/00 ,  G06T7/11

Abstract: Techniques disclosed herein facilitate tracking the degree to which a size of a biological structure changes over time. In some instances, an initial biological image (collected at a first time) can be segmented to characterized a boundary and size. A subsequent biological image can be processed to identify a deformation and/or transformation variable (e.g., one or more Jacobian matrices and/or one or more Jacobian determinants). The deformation and/or transformation variable(s) and initial segmentation can be used to predict a size of the biological structure at a subsequent time. The predicted size may inform a treatment recommendation.

公开(公告)号：US20230342935A1

公开(公告)日：2023-10-26

申请号：US18304006

申请日：2023-04-20

Applicant: Genentech, Inc.

Inventor： Neha Sutheekshna ANEGONDI ,  Simon Shang GAO ,  Jiaxiang JIANG ,  Michael Gregg KAWCZYNSKI ,  Jasmine PATIL ,  Theodore C. SPAIDE

IPC: G06T7/00 ,  G06T7/174 ,  G06T7/62 ,  A61B3/10 ,  A61B3/12

CPC classification number: G06T7/0014 ,  G06T7/174 ,  G06T7/62 ,  A61B3/102 ,  A61B3/1225 ,  G06T2207/10064 ,  G06T2207/10048 ,  G06T2207/10101 ,  G06T2207/30041 ,  G06T2207/30096 ,  G06T2207/20084 ,  G06T2207/20021

Abstract: A method and system for generating a geographic atrophy (GA) lesion segmentation mask corresponding to GA lesions in a retina is disclosed herein. In some embodiments, a set of fundus autofluorescence (FAF) images of a retina having one or more geographic atrophy (GA) lesions and one or both of a set of infrared (IR) images of the retina or a set of optical coherence tomography (OCT) images of the retina may be used to generate the GA lesion segmentation mask including one or more GA lesion segments corresponding to the one or more GA lesions in the retina. In some instances, a neural network may be used to generate the GA lesion segmentation mask.

公开(公告)号：US20220383621A1

公开(公告)日：2022-12-01

申请号：US17885221

申请日：2022-08-10

Applicant: Genentech, Inc.

Inventor： Jasmine PATIL

IPC: G06V10/764 ,  G06V10/82 ,  G06N3/08

Abstract: A data set can be provided that includes an input data element and one or more label data portion definitions that each identify a feature of interest within the input data element. A machine-learning model can generate model-identified portions definitions that identify predicted feature of interests within the input data element. At least one false negative (where a feature of interest is identified without a corresponding predicted feature of interest) and at least one false positive (where a predicted feature of interest is identified without a corresponding feature of interest) can be a identified. A class-disparate loss function can be provided that is configured to penalize false negatives more than at least some false positives. A loss can be calculated using the class-disparate loss function. A set of parameter values of the machine-learning model can be determined based on the loss.