Retinal Vessel Segmentation On Chase_Db1

Metrics

AUC

Acc

F1 score

MCC

Sensitivity

mIOU

Results

Performance results of various models on this benchmark

							Paper Title
RV-GAN	0.9914	-	0.8957	-	0.8199	0.9705	RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
Study Group Learning	0.9920	-	0.8271	-	0.8690	-	Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
MERIT-GCASCADE	-	-	0.8267	-	0.8493	0.7050	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
PVT-GCASCADE	-	-	0.8251	-	0.8584	0.7024	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
SA-UNet	0.9905	-	0.8153	-	-	-	SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
FR-UNet	0.9913	-	0.8151	-	0.8798	-	Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
FSG-Net	0.9937	0.9751	0.8101	0.7989	0.8599	0.8268	Full-scale Representation Guided Network for Retinal Vessel Segmentation
IterNet	0.9851	-	0.8073	-	-	-	IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
VGN	0.9830	-	0.8034	-	-	-	Deep Vessel Segmentation By Learning Graphical Connectivity
LadderNet	0.9839	-	0.8031	-	-	-	LadderNet: Multi-path networks based on U-Net for medical image segmentation
R2U-Net	0.9815	-	0.7928	-	-	-	Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
DUNet	0.9804	-	0.7883	-	-	-	DUNet: A deformable network for retinal vessel segmentation
Residual U-Net	0.9779	-	0.7800	-	-	-	Road Extraction by Deep Residual U-Net
U-Net ASPP	-	-	-	-	-	0.8959	Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks
U-Net	0.9772	-	-	-	-	-	U-Net: Convolutional Networks for Biomedical Image Segmentation

0 of 15 row(s) selected.

Retinal Vessel Segmentation On Chase_Db1

Metrics

AUC

Acc

F1 score

MCC

Sensitivity

mIOU

Results

Performance results of various models on this benchmark

							Paper Title
RV-GAN	0.9914	-	0.8957	-	0.8199	0.9705	RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
Study Group Learning	0.9920	-	0.8271	-	0.8690	-	Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
MERIT-GCASCADE	-	-	0.8267	-	0.8493	0.7050	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
PVT-GCASCADE	-	-	0.8251	-	0.8584	0.7024	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
SA-UNet	0.9905	-	0.8153	-	-	-	SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
FR-UNet	0.9913	-	0.8151	-	0.8798	-	Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
FSG-Net	0.9937	0.9751	0.8101	0.7989	0.8599	0.8268	Full-scale Representation Guided Network for Retinal Vessel Segmentation
IterNet	0.9851	-	0.8073	-	-	-	IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
VGN	0.9830	-	0.8034	-	-	-	Deep Vessel Segmentation By Learning Graphical Connectivity
LadderNet	0.9839	-	0.8031	-	-	-	LadderNet: Multi-path networks based on U-Net for medical image segmentation
R2U-Net	0.9815	-	0.7928	-	-	-	Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
DUNet	0.9804	-	0.7883	-	-	-	DUNet: A deformable network for retinal vessel segmentation
Residual U-Net	0.9779	-	0.7800	-	-	-	Road Extraction by Deep Residual U-Net
U-Net ASPP	-	-	-	-	-	0.8959	Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks
U-Net	0.9772	-	-	-	-	-	U-Net: Convolutional Networks for Biomedical Image Segmentation

0 of 15 row(s) selected.