Department of Applied Computing
Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs.
Journal of Advances in Applied & Computational Mathematics
Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms.
Journal of Advances in Applied & Computational Mathematics,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16812
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Copyright (c) 2022 Chen Zhao, Robert Bober, Haipeng Tang, Jinshan Tang, Minghao Dong, Chaoyang Zhang, Zhuo He, Michele L. Esposito, Zhihui Xu, Weihua Zhou. Publisher’s version of record: https://doi.org/10.15377/2409-5761.2022.09.6