Retinal Vessel Segmentation
46 papers with code • 8 benchmarks • 6 datasets
Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
( Image credit: LadderNet )
Benchmarks
These leaderboards are used to track progress in Retinal Vessel Segmentation
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Libraries
Use these libraries to find Retinal Vessel Segmentation models and implementations
DRIVE
STARE
CHASE_DB1
HRF
ROSE
SMDG
Latest papers with no code
SCOPE: Structural Continuity Preservation for Medical Image Segmentation
Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures.
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness.
A Trio-Method for Retinal Vessel Segmentation using Image Processing
Inner Retinal neurons are a most essential part of the retina and they are supplied with blood via retinal vessels.
Orientation and Context Entangled Network for Retinal Vessel Segmentation
In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels.
Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation
The best average of Hausdorff distance and mean square error were obtained using the Nested U-Net with the Dice loss function, which had an average of 6. 32 and 0. 0241 respectively.
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation
We validated the proposed method on retinal fundus images from the DRIVE database.
IDmUNet: A new image decomposition induced network for sparse feature segmentation
Because of the sparsity prior and deep unfolding method in the structure design, this IDmUNet combines the advantages of mathematical modeling and data-driven approaches.
SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss
Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase.
RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation
Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets.
Image Magnification Network for Vessel Segmentation in OCTA Images
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature.
