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 …

This paper presents a comprehensive review of retinal blood vessel segmentation based on
deep learning. The geometric characteristics of retinal vessels reflect the health status of …

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

Recent advancements in deep learning architectures have extended their application to
computer vision tasks, one of which is the segmentation of retinal blood vessels from retinal …

Segmentation is an essential requirement to accurately access diabetic retinopathy (DR)
and it becomes extremely time-consuming and challenging to detect manually. As a result …

Medical image segmentation plays a vital role in disease diagnosis and analysis. However,
data-dependent difficulties such as low image contrast, noisy background, and complicated …

Refined and automatic retinal vessel segmentation is crucial for computer-aided early
diagnosis of retinopathy. However, existing methods often suffer from mis-segmentation …

The class imbalance issue is a pretty common and enduring topic all the time. When
encountering unbalanced data distribution, conventional methods are prone to classify …

Accurate retinal vessel segmentation is challenging because of the complex texture of
retinal vessels and low imaging contrast. Previous methods generally refine segmentation …

Abstract Background and Objectives: Automatic segmentation of the cerebral vasculature
and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm …