Retinal Vessel Segmentation

37 papers with code • 4 benchmarks • 4 datasets

Retinal vessel segmentation is the task of segmenting vessels in retina imagery.

( Image credit: LadderNet )

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

labmlai/annotated_deep_learning_paper_implementations 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

Road Extraction by Deep Residual U-Net

rishikksh20/ResUnet 29 Nov 2017

Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

LeeJunHyun/Image_Segmentation 20 Feb 2018

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

sraashis/ature 19 Mar 2019

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.

LadderNet: Multi-path networks based on U-Net for medical image segmentation

juntang-zhuang/LadderNet 17 Oct 2018

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

woalsdnd/v-gan 28 Jun 2017

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

JiajieMo/OctaveUNet 28 Jun 2019

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

americofmoliveira/VesselSegmentation_ESWA 22 Nov 2019

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

conscienceli/IterNet 12 Dec 2019

Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.

Channel Attention Residual U-Net for Retinal Vessel Segmentation

clguo/CAR-UNet 7 Apr 2020

Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases.