The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

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 …

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 …

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 …

Retinal vasculature provides an opportunity for direct observation of vessel morphology,
which is linked to multiple clinical conditions. However, objective and quantitative …

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

The condition of the vascular network of human eye is an important diagnostic factor in
ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of …

Retinal blood vessel segmentation in fundus images plays an important role in the early
diagnosis and treatment of retinal diseases. In recent years, the segmentation methods …

We introduce a method for training neural networks to perform image or volume
segmentation in which prior knowledge about the topology of the segmented object can be …