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Cardiovascular disease (CVD) accounts for about half of non-communicablediseases. Vessel stenosis in the coronary artery is considered to be the majorrisk of CVD. Computed tomography angiography (CTA) is one of the widely usednoninvasive imaging modalities in coronary artery diagnosis due to its superiorimage resolution. Clinically, segmentation of coronary arteries is essentialfor the diagnosis and quantification of coronary artery disease. Recently, avariety of works have been proposed to address this problem. However, on onehand, most works rely on in-house datasets, and only a few works publishedtheir datasets to the public which only contain tens of images. On the otherhand, their source code have not been published, and most follow-up works havenot made comparison with existing works, which makes it difficult to judge theeffectiveness of the methods and hinders the further exploration of thischallenging yet critical problem in the community. In this paper, we propose alarge-scale dataset for coronary artery segmentation on CTA images. Inaddition, we have implemented a benchmark in which we have tried our best toimplement several typical existing methods. Furthermore, we propose a strongbaseline method which combines multi-scale patch fusion and two-stageprocessing to extract the details of vessels. Comprehensive experiments showthat the proposed method achieves better performance than existing works on theproposed large-scale dataset. The benchmark and the dataset are published athttps://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.

ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images