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.

Date of Patent:
Nov. 29, 2022

Filed:

Sep. 08, 2020
Applicant:

Shanghai United Imaging Intelligence Co., Ltd., Shanghai, CN;

Inventors:

Qiaoying Huang, Edison, NJ (US);

Shanhui Sun, Lexington, MA (US);

Zhang Chen, Brookline, MA (US);

Terrence Chen, Lexington, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/55 (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 7/73 (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 neural network-based systems, methods and instrumentalities associated with estimating a thickness of an anatomical structure based on a visual representation of the anatomical structure and a machine-learned thickness prediction model. The visual representation may include an image or a segmentation mask of the anatomical structure. The thickness prediction model may be learned based on ground truth information derived by applying a partial differential equation such as Laplace's equation to the visual representation and solving the partial differential equation. When the visual representation includes an image of the anatomical structure, the systems, methods and instrumentalities described herein may also be capable of generating a segmentation mask of the anatomical structure based on the image.


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