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
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network
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Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
lseventeen/FR-UNet
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JBHI 2022
The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity.
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
lkpengcs/rvms
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Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student.
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
limingxing00/retinal-vessel-segmentation-isbi2022
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Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast.
An Active Contour Model Using Matched Filter and Hessian Matrix for Retinal Vessels Segmentation
To prevent the destruction of the stability of evolution, we used an optimization process formula whose task is to keep contour on the edges of the image.
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
lkpengcs/dcda
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The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.
DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet
Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems.
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.
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.
