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:
Oct. 15, 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);

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

Xinyu Guo, Redmond, WA (US);

Qi Song, Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/30 (2018.01); G06N 3/045 (2023.01); G06T 7/00 (2017.01); G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G16H 50/30 (2018.01); G06N 3/045 (2023.01); G06T 7/0012 (2013.01); G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01);
Abstract

This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure. Then, an embed feature is obtained based on both the local feature and the global feature and input into to the node. The method is able to integrate local and global consideration factors of the sampling points into the GNN to improve the prediction accuracy.


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