  Cardiovascular disease (CVD) accounts for about half of non-communicable
diseases. Vessel stenosis in the coronary artery is considered to be the major
risk of CVD. Computed tomography angiography (CTA) is one of the widely used
noninvasive imaging modalities in coronary artery diagnosis due to its superior
image resolution. Clinically, segmentation of coronary arteries is essential
for the diagnosis and quantification of coronary artery disease. Recently, a
variety of works have been proposed to address this problem. However, on one
hand, most works rely on in-house datasets, and only a few works published
their datasets to the public which only contain tens of images. On the other
hand, their source code have not been published, and most follow-up works have
not made comparison with existing works, which makes it difficult to judge the
effectiveness of the methods and hinders the further exploration of this
challenging yet critical problem in the community. In this paper, we propose a
large-scale dataset for coronary artery segmentation on CTA images. In
addition, we have implemented a benchmark in which we have tried our best to
implement several typical existing methods. Furthermore, we propose a strong
baseline method which combines multi-scale patch fusion and two-stage
processing to extract the details of vessels. Comprehensive experiments show
that the proposed method achieves better performance than existing works on the
proposed large-scale dataset. The benchmark and the dataset are published at
https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.
