Coronary Wall Segmentation in CCTA Scans via a Hybrid Net with Contours Regularization

TL;DR

This paper introduces a hybrid deep learning architecture with a contour regularization loss for coronary artery wall segmentation in CCTA scans, achieving more accurate and connected boundaries than previous methods.

Contribution

A novel hybrid network architecture combined with a contour-constrained loss function to improve boundary continuity and accuracy in coronary artery segmentation.

Findings

Outperforms state-of-the-art methods in boundary accuracy

Produces smooth, closed coronary artery boundaries

Effective on a dataset of 34 patient scans

Abstract

Providing closed and well-connected boundaries of coronary artery is essential to assist cardiologists in the diagnosis of coronary artery disease (CAD). Recently, several deep learning-based methods have been proposed for boundary detection and segmentation in a medical image. However, when applied to coronary wall detection, they tend to produce disconnected and inaccurate boundaries. In this paper, we propose a novel boundary detection method for coronary arteries that focuses on the continuity and connectivity of the boundaries. In order to model the spatial continuity of consecutive images, our hybrid architecture takes a volume (i.e., a segment of the coronary artery) as input and detects the boundary of the target slice (i.e., the central slice of the segment). Then, to ensure closed boundaries, we propose a contour-constrained weighted Hausdorff distance loss. We evaluate our method on a dataset of 34 patients of coronary CT angiography scans with curved planar reconstruction (CCTA-CPR) of the arteries (i.e., cross-sections). Experiment results show that our method can produce smooth closed boundaries outperforming the state-of-the-art accuracy.