ECA-UNet for coronary artery segmentation and three-dimensional reconstruction

The narrowed area of coronary arteries is a common cardiovascular disease (CVD). Accurate computer-assisted treatment can find the location of the narrowed area in time and reduce the patient's life risk. This article designed a deeper residual U-Net and added the Efficient Channel Attention (ECA) module to segment Computed Tomography Angiography (CTA) images. The network is called ECA-UNet. ECA module can implement cross-channel interaction without dimensionality reduction. During our experiment, choosing an adaptive kernel size for the ECA module can effectively improve the network's performance. Experimental results proved that after selecting the adaptive cross-channel interaction coverage, the Intersection over Union (Iou) score of ECA-UNet on the testing set reaches 94.29%. Compared with other models in medical image segmentation, there is a pronounced improvement. For the segmented results, use the Ray-casting algorithm to reconstruct the three-dimensional coronary arteries, the narrowed area of the coronary artery can be clearly and intuitively observed from the reconstructed model.

Automated summary

This article introduces a deeper residual U-Net network with the Efficient Channel Attention (ECA) module for image segmentation of coronary artery stenosis. The experimental results demonstrate that the network outperforms other models in the testing set.