Retinal Vessel Segmentation
54 papers with code • 10 benchmarks • 8 datasets
Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
( Image credit: LadderNet )
Libraries
Use these libraries to find Retinal Vessel Segmentation models and implementationsMost implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
Dynamic Deep Networks for Retinal Vessel Segmentation
To address this limitation, we propose a novel, stochastic training scheme for deep neural networks that better classifies the faint, ambiguous regions of the image.
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension.
LadderNet: Multi-path networks based on U-Net for medical image segmentation
A LadderNet has more paths for information flow because of skip connections and residual blocks, and can be viewed as an ensemble of Fully Convolutional Networks (FCN).
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images.
Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network
Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions and octave transposed convolutions for learning multiple-spatial-frequency features, thus can better capture retinal vasculatures with varying sizes and shapes.
Retinal Vessel Segmentation based on Fully Convolutional Networks
The morphological attributes of retinal vessels, such as length, width, tortuosity and branching pattern and angles, play an important role in diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension and arteriosclerosis.
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.
Channel Attention Residual U-Net for Retinal Vessel Segmentation
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases.