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 …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

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 …

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio
(CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the …

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …

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 …

This paper tackles the task of semi-supervised video object segmentation, ie, the separation
of an object from the background in a video, given the mask of the first frame. We present …

Objective: Deep learning based methods for retinal vessel segmentation are usually trained
based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to …

In medical image analysis applications, the availability of the large amounts of annotated
data is becoming increasingly critical. However, annotated medical data is often scarce and …

Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related
diseases, in which both thick vessels and thin vessels are important features for symptom …