Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Coronary artery disease (CAD) is the leading cause of death worldwide, constituting more than one-fourth of global mortalities every year. Accurate semantic segmentation of each artery using invasive coronary angiography (ICA) is important for stenosis assessment and CAD diagnosis. However, due to the morphological similarity among different types of arteries, it is challenging for deep-learning-based models to generate semantic segmentation with an end-to-end approach. In this paper, we propose a multi-step semantic segmentation algorithm based on the analysis of 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 support vector machine. Each arterial segment is classified into left coronary artery (LCA), left anterior descending (LAD) and other types of arterial segments. We tested the proposed method on a dataset with 225 ICAs and achieved artery classification accuracy of 70.33%. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by a new faculty startup grant from Michigan Technological University Institute of Computing and Cybersystems (PI: Weihua Zhou) and a seed grant from Michigan Technological University Health Research Institute. This research was also supported in part by the National Institutes of Health under award numbers U19AG055373, R01GM109068, R01MH104680, R01MH107354 and by the National Science Foundation NSF under award number 1539067. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the ethics committee of The First Affiliated Hospital of Nanjing Medical University. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data is currently unavailable to the public.