{"url":"/dataset/chase-db1","name":"CHASE_DB1","full_name":"CHASE_DB1","description_markdown":"**CHASE_DB1** is a dataset for retinal vessel segmentation which contains 28 color retina images with the size of 999×960 pixels which are collected from both left and right eyes of 14 school children. Each image is annotated by two independent human experts.\r\n\r\nSource: [MixModule: Mixed CNN Kernel Module for Medical Image Segmentation](https://arxiv.org/abs/1910.08728)\r\nImage Source: [https://www.mdpi.com/2073-8994/9/11/276](https://www.mdpi.com/2073-8994/9/11/276)","description_withheld":null,"homepage":"https://blogs.kingston.ac.uk/retinal/chasedb1/","introduced_date":"2012-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation","first_author":null,"url":"https://doi.org/10.1109/TBME.2012.2205687"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Retinal Vessel Segmentation","url":"/task/retinal-vessel-segmentation","datasets_with_task":"/datasets/task/retinal-vessel-segmentation"}],"languages":[],"variants":["CHASE_DB1"],"data_loaders":[{"repo":"https://github.com/open-mmlab/mmsegmentation","url":"https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md","frameworks":["pytorch"]}],"num_papers_in_archive":59,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/retinal-vessel-segmentation-on-chase_db1","task":"Retinal Vessel Segmentation","dataset_variant":"CHASE_DB1","rows":16,"metrics":["AUC","F1 score","mIOU","Sensitivity","MCC","1:1 Accuracy","Acc","Average IOU","DSC"],"first_row_in_archive_order":{"model":"FSG-Net","paper":"/paper/full-scale-representation-guided-network-for","metrics":{"AUC":"0.9937","Acc":"0.9751","F1 score":"0.8101","MCC":"0.7989","Sensitivity":"0.8599","mIOU":"0.8268"},"code_links":[{"title":"zombasy/fsg-net-pytorch","url":"https://github.com/zombasy/fsg-net-pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-chase-db1","task":"Medical Image Segmentation","dataset_variant":"CHASE_DB1","rows":3,"metrics":["DSC"],"first_row_in_archive_order":{"model":"MERIT-GCASCADE","paper":"/paper/g-cascade-efficient-cascaded-graph","metrics":{"DSC":"0.8267"},"code_links":[{"title":"SLDGroup/G-CASCADE","url":"https://github.com/SLDGroup/G-CASCADE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dual-encoding-feature-filtering-generalized","title":"Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation","date":"2025-06-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/full-scale-representation-guided-network-for","title":"Full-scale Representation Guided Network for Retinal Vessel Segmentation","date":"2025-01-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/g-cascade-efficient-cascaded-graph","title":"G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation","date":"2023-10-24","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/resolution-aware-design-of-atrous-rates-for","title":"Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks","date":"2023-07-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/full-resolution-network-and-dual-threshold","title":"Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation","date":"2022-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fanet-a-feedback-attention-network-for","title":"FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation","date":"2021-03-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/study-group-learning-improving-retinal-vessel","title":"Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels","date":"2021-03-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rv-gan-retinal-vessel-segmentation-from","title":"RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network","date":"2021-01-03","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/sa-unet-spatial-attention-u-net-for-retinal","title":"SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation","date":"2020-04-07","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/iternet-retinal-image-segmentation-utilizing","title":"IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks","date":"2019-12-12","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/dunet-a-deformable-network-for-retinal-vessel","title":"DUNet: A deformable network for retinal vessel segmentation","date":"2018-11-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/laddernet-multi-path-networks-based-on-u-net","title":"LadderNet: Multi-path networks based on U-Net for medical image segmentation","date":"2018-10-17","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/deep-vessel-segmentation-by-learning","title":"Deep Vessel Segmentation By Learning Graphical Connectivity","date":"2018-06-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/recurrent-residual-convolutional-neural","title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","date":"2018-02-20","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/road-extraction-by-deep-residual-u-net","title":"Road Extraction by Deep Residual U-Net","date":"2017-11-29","rows_on_this_dataset":1,"code_links":13,"syntology":null},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":757,"samples_ran":510,"samples_unverified":247,"pointer_only_for_licence":426,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":774,"samples_ran":521,"samples_unverified":253,"pointer_only_for_licence":440,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}