US 11,776,149 B2
Prediction method for healthy radius of blood vessel path, prediction method for candidate stenosis of blood vessel path, and blood vessel stenosis degree prediction device
Xin Wang, Seattle, WA (US); Youbing Yin, Kenmore, WA (US); Junjie Bai, Seattle, WA (US); Yuwei Li, Bellevue, WA (US); Yi Lu, Seattle, WA (US); Kunlin Cao, Kenmore, WA (US); and Qi Song, Seattle, WA (US)
Assigned to KEYA MEDICAL TECHNOLOGY CO., LTD., Beijing (CN)
Filed by KEYA MEDICAL TECHNOLOGY CO., LTD., Beijing (CN)
Filed on Apr. 22, 2021, as Appl. No. 17/237,480.
Application 17/237,480 is a continuation of application No. 16/580,981, filed on Sep. 24, 2019, granted, now 11,030,765.
Claims priority of provisional application 62/735,829, filed on Sep. 24, 2018.
Claims priority of application No. 201910262838.0 (CN), filed on Apr. 2, 2019.
Prior Publication US 2021/0241484 A1, Aug. 5, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/62 (2017.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); A61B 5/02 (2006.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06T 7/00 (2017.01)
CPC G06T 7/62 (2017.01) [A61B 5/02007 (2013.01); A61B 5/7264 (2013.01); A61B 5/7275 (2013.01); A61B 6/032 (2013.01); A61B 6/504 (2013.01); G06T 7/0014 (2013.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30101 (2013.01); G06T 2207/30172 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for predicting a blood vessel stenosis, the method comprising:
extracting a blood vessel path and its centerline based on an image of a blood vessel;
determining a candidate stenosis for the blood vessel path;
identifying image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis; and
determining a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis,
wherein the convolutional neural network and the recurrent neural network are sequentially applied, wherein the convolutional neural network is applied on the image blocks along the centerline of the blood vessel path to generate vectors for the respective image blocks, wherein the recurrent neural network is applied on the vectors.