Retinal Vessel Segmentation

28 papers with code • 4 benchmarks • 4 datasets

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

( Image credit: LadderNet )

Greatest papers with code

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.

 Ranked #1 on Colorectal Gland Segmentation: on CRAG (DiceOC metric)

Cell Segmentation Colorectal Gland Segmentation: +7

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.

Image Classification Lesion Segmentation +3

Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

PaddlePaddle/Research 31 Jul 2020

In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.

Data Augmentation Retinal Vessel Segmentation

Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

ChengBinJin/V-GAN-tensorflow 28 Jun 2017

Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images.

Retinal Vessel Segmentation

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.

Lesion Segmentation Lung Nodule Segmentation +3

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

Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network

koshian2/OctConv-TFKeras 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

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

clguo/SA-UNet 7 Apr 2020

The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension.

Data Augmentation Retinal Vessel Segmentation