公开(公告)号：US12299890B2

公开(公告)日：2025-05-13

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

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.

公开(公告)号：US12115015B2

公开(公告)日：2024-10-15

申请号：US17473495

申请日：2021-09-13

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

CPC classification number: A61B6/5217 ,  A61B6/5235 ,  A61B6/5247 ,  G06N3/02 ,  G06T7/11 ,  G06T7/174 ,  G16H10/20 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20016 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06T2207/30242

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.