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
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.
Learning to Address Intra-segment Misclassification in Retinal Imaging
rmaphoh/Learning_AVSegmentation
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Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
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.
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
SharifAmit/RVGAN
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In order to avoid the loss of fidelity suffered by traditional GAN-based segmentation systems, we introduce a novel weighted feature matching loss.
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.
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 Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
agaldran/lwnet
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Our analysis demonstrates that the retinal vessel segmentation problem is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques.
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.
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.
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.
