A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning

Abstract

Automatic detection of thin-cap fibroatheroma (TCFA) is essential to prevent acute coronary syndrome. Hence, in this paper, a method is proposed to detect TCFAs by directly classifying each A-line using multi-view intravascular optical coherence tomography (IVOCT) images. To solve the problem of false positives, a multi-input–output network was developed to implement image-level classification and A-line-based classification at the same time, and a contrastive consistency term was designed to ensure consistency between two tasks. In addition, to learn spatial and global information and obtain the complete extent of TCFAs, an architecture and a regional connectivity constraint term are proposed to classify each A-line of IVOCT images. Experimental results obtained on the 2017 China Computer Vision Conference IVOCT dataset show that the proposed method achieved state-of-art performance with a total score of 88.7 ± 0.88 % , overlap rate of 88.64 ± 0.26 % , precision rate of 84.34 ± 0.86 % , and recall rate of 93.67 ± 2.29 % .