  Coronary artery disease (CAD) has posed a leading threat to the lives of
cardiovascular disease patients worldwide for a long time. Therefore, automated
diagnosis of CAD has indispensable significance in clinical medicine. However,
the complexity of coronary artery plaques that cause CAD makes the automatic
detection of coronary artery stenosis in Coronary CT angiography (CCTA) a
difficult task. In this paper, we propose a Transformer network (TR-Net) for
the automatic detection of significant stenosis (i.e. luminal narrowing > 50%)
while practically completing the computer-assisted diagnosis of CAD. The
proposed TR-Net introduces a novel Transformer, and tightly combines
convolutional layers and Transformer encoders, allowing their advantages to be
demonstrated in the task. By analyzing semantic information sequences, TR-Net
can fully understand the relationship between image information in each
position of a multiplanar reformatted (MPR) image, and accurately detect
significant stenosis based on both local and global information. We evaluate
our TR-Net on a dataset of 76 patients from different patients annotated by
experienced radiologists. Experimental results illustrate that our TR-Net has
achieved better results in ACC (0.92), Spec (0.96), PPV (0.84), F1 (0.79) and
MCC (0.74) indicators compared with the state-of-the-art methods. The source
code is publicly available from the link (https://github.com/XinghuaMa/TR-Net).
