The detection of curvilinear structures in medical images, eg, blood vessels or nerve fibers,
is important in aiding management of many diseases. In this work, we propose a general …

Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …

Curvilinear structure segmentation (CSS) is under semantic segmentation, whose
applications include crack detection, aerial road extraction, and biomedical image …

Corneal endothelial cell segmentation plays a vital role in quantifying clinical indicators such
as cell density, coefficient of variation, and hexagonality. However, the corneal …

The vessel-like structure in biomedical images, such as within cerebrovascular and nervous
pathologies, is an essential biomarker in understanding diseases' mechanisms and in …

Retinal fundus vessels contain rich geometric features, including both thick and thin vessels,
which is particularly important for accurate clinical diagnosis of cardiovascular diseases …

U-Net has been providing state-of-the-art performance in many medical image segmentation
problems. Many modifications have been proposed for U-Net, such as attention U-Net …

Segmentation of 2D images is a fundamental problem for biomedical image analysis. The
most widely used architecture for biomedical image segmentation is U-Net. U-Net introduces …

Blood vessel segmentation in fundus images is a critical procedure in the diagnosis of
ophthalmic diseases. Recent deep learning methods achieve high accuracy in vessel …

Medical image segmentation is an important step in medical image analysis. With the rapid
development of a convolutional neural network in image processing, deep learning has …