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:
Sep. 17, 2024

Filed:

Apr. 21, 2022
Applicant:

Shenzhen Keya Medical Technology Corporation, Shenzhen, CN;

Inventors:

Xin Wang, Seattle, WA (US);

Youbing Yin, Kenmore, WA (US);

Bin Kong, Charlotte, NC (US);

Yi Lu, Seattle, WA (US);

Xinyu Guo, Redmond, WA (US);

Hao-Yu Yang, Seattle, WA (US);

Junjie Bai, Seattle, WA (US);

Qi Song, Seattle, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/045 (2023.01); G06V 20/70 (2022.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06N 3/045 (2023.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01);
Abstract

The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.


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