US 12,094,596 B2
Method and system for anatomical labels generation
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); 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. 17/726,039.
Claims priority of provisional application 63/178,894, filed on Apr. 23, 2021.
Prior Publication US 2022/0344033 A1, Oct. 27, 2022
Int. Cl. G06V 10/82 (2022.01); G06N 3/045 (2023.01); G06V 20/70 (2022.01); G16H 30/40 (2018.01)
CPC G16H 30/40 (2018.01) [G06N 3/045 (2023.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating anatomical labels of an anatomical structure, comprising:
receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and
predicting, by at least one processor, the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network, wherein the deep learning network comprises a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series, wherein the branched network comprises at least two branch networks in parallel.