Automatic Location and Classification of Coronary Artery Stenosis Based on Deep Neural Network
Cong Chao1,2, Xiao Zhaohui3, Chen Wenjun2, Wang Yi1*
1(Army Medical Center of PLA, Army Medical University, Chongqing 400016, China) 2(School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China) 3(College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China)
Abstract：In this paper, a deep neural network-based workflow was proposed to automatically detect and classify the stenosis features in coronary images. The algorithm mainly used quantitative coronary angiography (QCA) as a label for supervised learning and classifies the severity of coronary stenosis into normal (< 25% stenosis score) and stenosis (> 25% stenosis) categories and realized stenosis location detection in images. The algorithm used the inception model as the basic classifier to preliminarily classify the image level stenosis, and then combined with the multi-level pool structure to jointly predict the multi perspective angiography image to obtain the left-/right-artery/patient level stenosis prediction. On the basis of the classifier, the feature was further extracted, and the unsupervised learning model was used to realize the narrow location in the image.The training and cross validation were performed on a total of 10872 images in 235 clinical studies. The results showed that the algorithm achieved 85% accuracy and 0.91 AUC score in image level stenosis classification; in multi view joint prediction experiment, the sensitivity and AUC score of 0.94/0.90/0.96 and 0.87/0.88/0.86 respectively for left-/ right-/patient level stenosis classification prediction. In the stenosis localization experiment, the sensitivity of detection for left-/right-artery stenosis was 0.70/0.68, and the mean square error of 512 × 512 image was 37.6/39.3 pixels, respectively. In conclusion, the proposed method realized the potential of auxiliary diagnosis prediction from image to patient with high accuracy, which not only provided the preliminary screening ability in the process of coronary angiography, but also laid the foundation for more accurate and automatic computer-aided diagnosis.
丛超, 肖朝晖, 陈文俊, 王毅. 基于深度神经网络的冠脉造影图像的血管狭窄自动定位及分类预测[J]. 中国生物医学工程学报, 2021, 40(3): 301-309.
Cong Chao, Xiao Zhaohui, Chen Wenjun, Wang Yi. Automatic Location and Classification of Coronary Artery Stenosis Based on Deep Neural Network. Chinese Journal of Biomedical Engineering, 2021, 40(3): 301-309.
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