公开(公告)号：US20240296567A1

公开(公告)日：2024-09-05

申请号：US18664469

申请日：2024-05-15

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Zhe XU ,  Donghuan LU ,  Yefeng ZHENG

IPC: G06T7/10 ,  G06V20/70 ,  G16H30/40

CPC classification number: G06T7/10 ,  G06V20/70 ,  G16H30/40

Abstract: Disclosed are a medical image segmentation method and apparatus, a device, a storage medium, and a program product, which relate to the field of artificial intelligence (AI). The method includes: performing image segmentation on a sample medical image through a source domain segmentation model, to obtain a first segmentation result, the source domain segmentation model being obtained through training based on image data in a source domain, the sample medical image being an unannotated image in a target domain; performing image segmentation on the sample medical image through a target domain segmentation model, to obtain a second segmentation result; correcting the first segmentation result based on the second segmentation result and a segmentation confidence level of the target domain segmentation model, to obtain a corrected segmentation result; and updating training on the target domain segmentation model based on the second segmentation result and the corrected segmentation result.

公开(公告)号：US20230343063A1

公开(公告)日：2023-10-26

申请号：US18216918

申请日：2023-06-30

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Zhe XU ,  Donghuan LU ,  Kai MA ,  Yefeng ZHENG

IPC: G06V10/26 ,  G06V20/70 ,  G06V10/82 ,  G06V10/77 ,  G06V10/74 ,  G06T7/194 ,  G06V10/776 ,  G06V10/774

CPC classification number: G06V10/267 ,  G06V20/70 ,  G06V10/82 ,  G06V10/7715 ,  G06V10/761 ,  G06T7/194 ,  G06V10/776 ,  G06V10/774 ,  G06T2207/20084 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06V2201/03 ,  G06T2207/30096 ,  G06T2207/30016 ,  G06T2207/30084

Abstract: An image segmentation model training method includes acquiring a first image, a second image, and a labeled image of the first image; acquiring a first predicted image according to a first network model; acquiring a second predicted image according to a second network model; determining a reference image of the second image based on the second image and the labeled image of the first image; and updating a model parameter of the first network model based on the first predicted image, the labeled image, the second predicted image, and the reference image to obtain an image segmentation model.