US 12,119,117 B2
Method and system for disease quantification of anatomical structures
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); and Qi Song, Seattle, WA (US)
Assigned to SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION, Shenzhen (CN)
Filed by SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION, Shenzhen (CN)
Filed on Apr. 21, 2022, as Appl. No. 17/726,307.
Claims priority of provisional application 63/178,940, filed on Apr. 23, 2021.
Prior Publication US 2022/0351863 A1, Nov. 3, 2022
Int. Cl. 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)
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)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of predicting disease quantification parameters for an anatomical structure, comprising:
receiving a medical image containing the anatomical structure;
extracting, by at least one processor, a centerline structure based on the medical image; and
predicting the disease quantification parameter for each sampling point the extracted centerline structure by using a graph neural network (GNN), wherein each node of the GNN corresponds to a sampling point on the extracted centerline structure and each edge of the GNN corresponds to a spatial constraint relationship between two sampling points, wherein predicting the disease quantification parameter for each sampling point further comprises:
extracting a local feature based on an image patch for the sampling point by using a local feature encoder;
extracting a global feature by using a global feature encoder based on a set of image patches for a set of sampling points, which include the sampling point and have a spatial constraint relationship defined by the centerline structure; and
obtaining an embed feature based on both the local feature and the global feature and inputting the embed feature to the node of the GNN corresponding to the sampling point.