The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
May. 17, 2022
Filed:
Sep. 08, 2020
Applicant:
Shanghai United Imaging Intelligence Co., Ltd., Shanghai, CN;
Inventors:
Assignee:
SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD., Shanghai, CN;
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G16H 50/50 (2018.01); G16H 50/30 (2018.01); G16H 30/40 (2018.01); G06F 3/0485 (2022.01); G06T 11/20 (2006.01); G06T 13/80 (2011.01); G06T 19/00 (2011.01); G06T 7/55 (2017.01); G06T 7/73 (2017.01); G06T 7/246 (2017.01); A61B 5/00 (2006.01); A61B 5/11 (2006.01); G06T 3/00 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/0044 (2013.01); A61B 5/1128 (2013.01); A61B 5/7264 (2013.01); G06F 3/0485 (2013.01); G06K 9/6267 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 3/0093 (2013.01); G06T 7/0014 (2013.01); G06T 7/11 (2017.01); G06T 7/248 (2017.01); G06T 7/55 (2017.01); G06T 7/73 (2017.01); G06T 11/206 (2013.01); G06T 13/80 (2013.01); G06T 19/00 (2013.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G06T 2200/24 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01); G06T 2210/41 (2013.01);
Abstract
Described herein are systems, methods and instrumentalities associated with image segmentation. The systems, methods and instrumentalities have a hierarchical structure for producing a coarse segmentation of an anatomical structure and then refining the coarse segmentation based on a shape prior of the anatomical structure. The coarse segmentation may be generated using a multi-task neural network and based on both a segmentation loss and a regression loss. The refined segmentation may be obtained by deforming the shape prior using one or more of a shape-based model or a learning-based model.