公开(公告)号：US20220230310A1

公开(公告)日：2022-07-21

申请号：US17665932

申请日：2022-02-07

Applicant: Genentech, Inc.

Inventor： Luke XIE ,  Kai Henrik BARCK ,  Omid BAZGIR

IPC: G06T7/00 ,  G06V10/764 ,  G06T7/70 ,  G06T7/10 ,  G06T7/62 ,  G06V10/75 ,  G06V20/64

Abstract: The present disclosure relates to techniques for segmenting objects within medical images using a deep learning network that is localized with object detection based on a derived contrast mechanism. Particularly, aspects are directed to localizing an object of interest within a first medical image having a first characteristic, projecting a bounding box or segmentation mask of the object of interest onto a second medical image having a second characteristic to define a portion of the second medical image, and inputting the portion of the second medical image into a deep learning model that is constructed as a detector using a weighted loss function capable of segmenting the portion of the second medical image and generating a segmentation boundary around the object of interest. The segmentation boundary may be used to calculate a volume of the object of interest for determining a diagnosis and/or a prognosis of a subject.

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.