Retinal Vessel Segmentation On Drive

Metrics

AUC

F1 score

Results

Performance results of various models on this benchmark

			Paper Title	Repository
FSG-Net	0.9823	0.8322	Full-scale Representation Guided Network for Retinal Vessel Segmentation
Study Group Learning	0.9886	0.8316	Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
FR-UNet	0.9889	0.8316	Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation	-
MERIT-GCASCADE	-	0.8290	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
SA-UNet	0.9864	0.8263	SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
VGN	0.9802	0.8263	Deep Vessel Segmentation By Learning Graphical Connectivity
ConvMixer	-	0.8245	Deep Learning Architectures for Diagnosis of Diabetic Retinopathy	-
DUNet	0.9802	0.8237	DUNet: A deformable network for retinal vessel segmentation	-
BCDU-Net (d=3)	0.9789	0.8224	Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
ConvMixer-Light	-	0.8215	Deep Learning Architectures for Diagnosis of Diabetic Retinopathy	-
PVT-GCASCADE	-	0.8210	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
IterNet	0.9816	0.8205	IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
LadderNet	0.9793	0.8202	LadderNet: Multi-path networks based on U-Net for medical image segmentation
Residual U-Net	0.9779	0.8149	Road Extraction by Deep Residual U-Net
U-Net	0.9755	0.8142	U-Net: Convolutional Networks for Biomedical Image Segmentation
DR_2021	-	0.75	Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images
ET-Net	-	-	ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
RV-GAN	-	-	RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
U-Net	0.9855	-	Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation
CE-Net	0.9779	-	CE-Net: Context Encoder Network for 2D Medical Image Segmentation

0 of 21 row(s) selected.

Command Palette

Retinal Vessel Segmentation On Drive

Metrics

Results