Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the
vessel segmentation method based on deep learning has achieved outstanding …

Background and objective Deep learning techniques are instrumental in developing network
models that aid in the early diagnosis of life-threatening diseases. To screen and diagnose …

In this paper, we are the first to propose a new graph convolution-based decoder namely,
Cascaded Graph Convolutional Attention Decoder (G-CASCADE), for 2D medical image …

Coronary artery disease (CAD) is one of the diseases with high mortality, and its diagnosis is
often facilitated by coronary artery segmentation in coronary computed tomography …

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary
arteries, affecting the blood supply to the heart, one of the leading causes of death around …

Retinal vessel segmentation (RVS) is crucial in medical image analysis as it helps identify
and monitor retinal diseases. Deep learning approaches have shown promising results for …

Medical image segmentation has been significantly advanced with the rapid development of
deep learning (DL) techniques. Existing DL-based segmentation models are typically …

Coronary angiography is the gold standard for diagnosing coronary artery disease.
Accurately detecting vessels and classifying vascular stenosis through coronary …

With convolutional neural networks' remarkable performance in computer vision, more and
more studies are applying deep learning to vessel image segmentation tasks. This work …

MedSegBench is a comprehensive benchmark designed to evaluate deep learning models
for medical image segmentation across a wide range of modalities. It covers a wide range of …