The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Big medical data mainly include electronic health record data, medical image data, gene
information data, etc. Among them, medical image data account for the vast majority of …

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 …

In recent years, deep learning-based networks have achieved state-of-the-art performance
in medical image segmentation. Among the existing networks, U-Net has been successfully …

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis,
early treatment and surgery planning of ocular diseases. Recently, deep learning based …

The precise segmentation of retinal blood vessels is of great significance for early diagnosis
of eye-related diseases such as diabetes and hypertension. In this work, we propose a …

Automatic segmentation of retinal vessels in fundus images plays an important role in the
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique
that has been increasingly used to image the retinal vasculature at capillary level resolution …

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …