Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries

TL;DR

This paper introduces TR-Net, a Transformer-based neural network that combines convolutional layers and Transformer encoders to automatically detect significant coronary artery stenosis in CCTA images, aiding CAD diagnosis.

Contribution

The paper proposes a novel Transformer network architecture that effectively integrates local and global image features for stenosis detection in CCTA images.

Findings

Achieved 92% accuracy in stenosis detection

Outperformed state-of-the-art methods in multiple metrics

Demonstrated effectiveness on a diverse patient dataset

Abstract

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).