Segmentation of curvilinear structures is important in many applications, such as retinal
blood vessel segmentation for early detection of vessel diseases and pavement crack …

Curvilinear object segmentation is critical for many applications. However, manually
annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently …

Curvilinear structures are frequently observed in various images in different forms, such as
blood vessels or neuronal boundaries in biomedical images. In this paper, we propose a …

Automatic segmentation of curvilinear objects in medical images plays an important role in
the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the …

Deep learning has shown great potential for curvilinear structure (eg, retinal blood vessels
and neurites) segmentation as demonstrated by a recent auto-context regression …

Image segmentation is a long-standing problem in medical image analysis to facilitate the
clinical diagnosis and intervention. Progress has been made due to deep learning via …

Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This
problem faces several challenges including low contrast, variable vessel size and thickness …

Convolutional neural networks have been widely applied to medical image segmentation
and have achieved considerable performance. However, the performance may be …

Learning structural information is critical for producing an ideal result in retinal image
segmentation. Recently, convolutional neural networks have shown a powerful ability to …

Retinal vessels are important biomarkers for many ophthalmological and cardiovascular
diseases. Hence, it is of great significance to develop automatic models for computer-aided …