Vascular structure segmentation plays a crucial role in medical analysis and clinical
applications. The practical adoption of fully supervised segmentation models is impeded by …

Although existing medical image segmentation methods provide impressive pixel-wise
accuracy, they often neglect topological correctness, making their segmentations unusable …

Convolutional neural networks (CNN) and Transformer variants have emerged as the
leading medical image segmentation backbones. Nonetheless, due to their limitations in …

Tooth instance segmentation of dental panoramic X-ray images represents a task of
significant clinical importance. Teeth demonstrate symmetry within the upper and lower …

Aneurysm segmentation in computed tomography angiography (CTA) images is essential
for medical intervention aimed at preventing subarachnoid hemorrhages. However, most …

Automatic labeling of coronary arteries is an essential task in the practical diagnosis process
of cardiovascular diseases. For experienced radiologists, the anatomically predetermined …

Automatic detection of retinal vasculature in optical coherence tomography angiography
(OCTA) images faces several challenges such as the closely located capillaries, vessel …

Over the past decades, medical image segmentation methods have been intensively
developed, but there are still a number of unsolved challenging problems in vascular image …

Systolic and diastolic registration of coronary arteries is a critical yet challenging step in
coronary artery disease analysis. Most existing methods ignore the important relationship …

Centerline tracking is useful in performing segmental analysis of vessel tortuosity in
angiography data. However, a highly tortuous) artery can produce multiple centerlines due …