The reliable segmentation of retinal vasculature can provide the means to diagnose and
monitor the progression of a variety of diseases affecting the blood vessel network, including …

Deep learning has recently yielded impressive gains in retinal vessel segmentation.
However, state-of-the-art methods tend to be conservative, favoring precision over recall …

Retinal vessel segmentation is of great significance for the clinical diagnosis of eye-related
diseases and diabetic retinopathy. CNN-based models have led to advancements in the …

While various deep learning models have recently been proposed or applied to improve the
segmentation of vessels in retinal images, the performance gap between different models …

Accurate vessel segmentation in retinal images is vital for retinopathy diagnosis and
analysis. However, existence of very thin vessels in low image contrast along with …

Retinal vessels identification plays a critical role in computer-aided diagnosis and analysis
of fundus images. While Deep-Learning-based segmentation methods have shown …

We develop a connection sensitive attention U-Net (CSAU) for accurate retinal vessel
segmentation. This method improves the recent attention U-Net for semantic segmentation …

State-of-the-art methods for retinal vessel segmentation mainly rely on manually labeled
vessels as the ground truth for supervised training. The quality of manual labels plays an …

Retinal vessel segmentation is a fundamental and crucial step to develop a computer-aided
diagnosis (CAD) system for retinal images. Retinal vessels appear as multiscaled tubular …

Over recent years, increasingly complex approaches based on sophisticated convolutional
neural network architectures have been slowly pushing performance on well-established …