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
Most implemented papers
An Elastic Interaction-Based Loss Function for Medical Image Segmentation
charrywhite/elastic_interaction_based_loss
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The commonly used loss functions in the deep segmentation task are pixel-wise loss functions.
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network
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To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.
Robust Retinal Vessel Segmentation from a Data Augmentation Perspective
In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.
Residual Spatial Attention Network for Retinal Vessel Segmentation
clguo/RSAN
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In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation.
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
browatbn2/VLight
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We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training.
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
SHI-Labs/SGL-Retinal-Vessel-Segmentation
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Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases.
Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation
onurboyar/Retinal-Vessel-Segmentation
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By analyzing input images and performing the augmentation accordingly we show that the performance of the U-Net model can be increased dramatically.
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e. g., diabetic, hypertensive and solar retinopathies.
A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers
Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores.
DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet
Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems.
