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：US20250000473A1

公开(公告)日：2025-01-02

申请号：US18883384

申请日：2024-09-12

Applicant: GENENTECH, INC.

Inventor： Nils Gustav Thomas BENGTSSON ,  Richard Alan Duray CARANO ,  Alexander James Stephen CHAMPION DE CRESPIGNY ,  Jill Osborn FREDRICKSON ,  Mohamed Skander JEMAA

IPC: A61B6/00 ,  G06N3/02 ,  G06T7/11 ,  G06T7/174 ,  G16H10/20 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50

Abstract: The present disclosure relates to techniques for segmenting tumors with positron emission tomography (PET) using deep convolutional neural networks for image and lesion metabolism analysis. Particularly, aspects of the present disclosure are directed to obtaining a PET scans and computerized tomography (CT) or magnetic resonance imaging (MRI) scans for a subject, preprocessing the PET scans and the CT or MRI scans to generate standardized images, generating two-dimensional segmentation masks, using two-dimensional segmentation models implemented as part of a convolutional neural network architecture that takes as input the standardized images, generating three-dimensional segmentation masks, using three-dimensional segmentation models implemented as part of the convolutional neural network architecture that takes as input patches of image data associated with segments from the two-dimensional segmentation mask, and generating a final imaged mask by combining information from the two-dimensional segmentation masks and the three-dimensional segmentation masks.

公开(公告)号：US20220319008A1

公开(公告)日：2022-10-06

申请号：US17842542

申请日：2022-06-16

Applicant: Genentech, Inc.

Inventor： Nils Gustav Thomas BENGTSSON ,  Richard Alan Duray CARANO ,  Alexander James Stephen CHAMPION DE CRESPIGNY ,  Jill Osborn FREDRICKSON ,  Mohamed Skander JEMAA

IPC: G06T7/00 ,  G06T7/12 ,  G06T7/70 ,  G06T7/62 ,  A61B5/00

Abstract: Medical image(s) are input into a detection network to generate mask(s) identifying a set of regions within the medical image(s), where the detection network predicts that each region identified in the mask(s) includes a depiction of a tumor of one or more tumors within the subject. For each region, the region of the medical image(s) is processed using a tumor segmentation network to generate one or more tumor segmentation boundaries for the tumor present within the subject. For each tumor and by using a plurality of organ-specific segmentation networks, an organ is determined within which at least part of the tumor is located. An output is generated based on the one or more tumor segmentation boundaries and locations of the organs within which at least part of the one or more tumors are located.