Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning

TL;DR

This paper presents a deep learning approach for segmenting coronary arteries in non-contrast CT images, utilizing a novel semi-automatic ground truth generation method that improves accuracy and generalization.

Contribution

It introduces a new framework for semi-automatic ground truth creation via image registration, enhancing segmentation accuracy in non-contrast cardiac CT images.

Findings

Model achieves higher accuracy than training ground truth.

Dice and clDice metrics close to interrater variability.

Efficient data generation improves model generalization.

Abstract

Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of coronary artery disease. Although various methods exist that offer high-quality segmentation of coronary arteries in cardiac contrast-enhanced CT scans, the potential of less invasive, non-contrast CT in this area is still not fully exploited. Since such fine anatomical structures are hardly visible in this type of medical images, the existing methods are characterized by high recall and low precision, and are used mainly for filtering of atherosclerotic plaques in the context of calcium scoring. In this paper, we address this research gap and introduce a deep learning algorithm for segmenting coronary arteries in multi-vendor ECG-gated non-contrast cardiac CT images which benefits from a novel framework for semi-automatic generation of Ground Truth (GT) via image registration. We hypothesize that the proposed GT generation process is much more efficient in this case than manual segmentation, since it allows for a fast generation of large volumes of diverse data, which leads to well-generalizing models. To investigate and thoroughly evaluate the segmentation quality based on such an approach, we propose a novel method for manual mesh-to-image registration, which is used to create our test-GT. The experimental study shows that the trained model has significantly higher accuracy than the GT used for training, and leads to the Dice and clDice metrics close to the interrater variability.