CASMatching strategy for automated detection and quantification of carotid artery stenosis based on digital subtraction angiography

• Published in: Computer methods and programs in biomedicine 2024-01, Vol.243, p.107871-107871, Article 107871
• Main Authors: Wulamu, Aziguli, Luo, Jichang, Chen, Saian, Zheng, Han, Wang, Tao, Yang, Renjie, Jiao, Liqun, Zhang, Taohong
• Format: Article
• Language: English
• Subjects: Carotid artery stenosis; Deep learning; Digital subtraction angiography; Object detection; Visual regression
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107871

•A novel solution to accurately predict morphological indices of carotid artery stenosis based on digital subtraction angiography, improving interpretability and accuracy.•A customized detection strategy named CASMatching to model the matching relationship between a stenosis and multiple normal vessel segments, thus identifying the normal vessel segment suitable for predicting reference vessel diameter.•A novel design of object detector head to predict the Match-ness score of multiple normal vessel segments, indicating the suitability for predicting the reference vessel diameter of the stenosis.•An improved DIoU loss named 2D-IoU is designed to introduce prior knowledge based on the vascular trend and improves the model performance. Automated detection and quantification of carotid artery stenosis is a crucial task in establishing a computer-aided diagnostic system for brain diseases. Digital subtraction angiography (DSA) is known as the "gold standard" for carotid stenosis diagnosis. It is commonly used to identify carotid artery stenosis and measure morphological indices of the stenosis. However, using deep learning to detect stenosis based on DSA images and further quantitatively predicting the morphological indices remain a challenge due the absence of prior work. In this paper, we propose a quantitative method for predicting morphological indices of carotid stenosis. Our method adopts a two-stage pipeline, first locating regions suitable for predicting morphological indices by object detection model, and then using a regression model to predict indices. A novel Carotid Artery Stenosis Matching (CASMatching) strategy is introduced into the object detection to model the matching relationship between a stenosis and multiple normal vessel segments. The proposed Match-ness branch predicts a Match-ness score for each normal vessel segment to indicate the degree of matching to the stenosis. A novel Direction Distance-IoU (2DIoU) loss based on the Distance-IoU loss is proposed to make the model focused more on the bounding box regression in the direction of vessel extension. After detection, the normal vessel segment with the highest Match-ness score and the stenosis are intercepted from the original image, then fed into a regression model to predict morphological indices and calculate the degree of stenosis. Our method is trained and evaluated on a dataset collected from three different manufacturers' monoplane X-ray systems. The results show that the propos