Background Blood vessel segmentation is a topic of high interest in medical image analysis
since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and …

The eye affords a unique opportunity to inspect a rich part of the human microvasculature
non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are …

Accurate medical image segmentation is essential for diagnosis, surgical planning and
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …

Abstract This paper presents Deep Retinal Image Understanding (DRIU), a unified
framework of retinal image analysis that provides both retinal vessel and optic disc …

Retinal vessel segmentation is a fundamental step for various ocular imaging applications.
In this paper, we formulate the retinal vessel segmentation problem as a boundary detection …

Goal: In this work, we present an extensive description and evaluation of our method for
blood vessel segmentation in fundus images based on a discriminatively trained fully …

Automatic segmentation of the retinal vasculature and the optic disc is a crucial task for
accurate geometric analysis and reliable automated diagnosis. In recent years …

Background and objectives: Diabetic retinopathy (DR) is one of the leading causes of
preventable blindness in the world. Its earliest sign are red lesions, a general term that …

We propose a novel deep learning based system for vessel segmentation. Existing methods
using CNNs have mostly relied on local appearances learned on the regular image grid …

The segmentation of blood vessels through color fundus images is a difficult and time-
consuming task that requires experienced clinicians. Recently, researchers have shown that …