CHASE_DB1

Overview edit

Variants: CHASE_DB1

Associated Benchmarks

This dataset is used in 2 benchmarks:

• Medical Image Segmentation - Metrics: DSC
• Retinal Vessel Segmentation - Metrics: AUC, F1 score, mIOU, Sensitivity, MCC, 1:1 Accuracy, Acc, Average IOU, DSC

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation (2025) -
• Full-scale Representation Guided Network for Retinal Vessel Segmentation (2025) -
• G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation (2023) -
• Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks (2023) -
• FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation (2021) -
• Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels (2021) -
• RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network (2021) -
• SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation (2020) -
• IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks (2019) -
• DUNet: A deformable network for retinal vessel segmentation (2018) -
• LadderNet: Multi-path networks based on U-Net for medical image segmentation (2018) -
• Deep Vessel Segmentation By Learning Graphical Connectivity (2018) -
• Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation (2018) -
• Road Extraction by Deep Residual U-Net (2017) -
• U-Net: Convolutional Networks for Biomedical Image Segmentation (2015) -