Clinically, retinal vessel segmentation is a significant step in the diagnosis of fundus
diseases. However, recent methods generally neglect the difference of semantic information …

By integrating visual and linguistic understanding, visual–language foundation models
(VLFMs) have the great potential to advance the interpretation of medical data, thereby …

Curvilinear object segmentation is critical for many applications. However, manually
annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently …

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 …

Medical images, especially intricate vascular structures, are costly and time-consuming to
annotate manually. It is beneficial to investigate an unsupervised method for vessel …

Retinal vessel segmentation plays a crucial role in the diagnosis and treatment of ocular
pathologies. Current methods have limitations in feature fusion and face challenges in …

Vessel segmentation is crucial in many medical image applications, such as detecting
coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high …

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

Abstract Background and Objective: Accurate segmentation of electron microscopy (EM)
volumes of the brain is essential to characterize neuronal structures at a cell or organelle …

In medical image analysis, blood vessel segmentation is of considerable clinical value for
diagnosis and surgery. The predicaments of complex vascular structures obstruct the …