Abstract Background and Objective: Vessel segmentation is the first processing stage of 3D
medical images for both clinical and research use. Current segmentation methods are …

Efficiently obtaining a reliable coronary artery centerline from computed tomography
angiography data is relevant in clinical practice. Whereas numerous methods have been …

Though conventional coronary angiography (CCA) has been the standard of reference for
diagnosing coronary artery disease in the past decades, computed tomography …

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality
for the visualization of the heart and coronary arteries. To fully exploit the potential of the …

A multiple hypothesis tracking approach to the segmentation of small 3D vessel structures is
presented. By simultaneously tracking multiple hypothetical vessel trajectories, low contrast …

Accurate detection and quantification of coronary artery stenoses is an essential
requirement for treatment planning of patients with suspected coronary artery disease. We …

Automatic blood vessel extraction from 3D medical images is crucial for vascular disease
diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer …

In this paper, we present a tubular structure segmentation method that utilizes a second
order tensor constructed from directional intensity measurements, which is inspired from …

This paper presents a vessel segmentation method which learns the geometry and
appearance of vessels in medical images from annotated data and uses this knowledge to …

Automatic coronary centerline extraction and lumen segmentation facilitate the diagnosis of
coronary artery disease (CAD), which is a leading cause of death in developed countries …