{"url":"/dataset/miccai-2015-multi-atlas-abdomen-labeling","name":"MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge","full_name":null,"description_markdown":"Under Institutional Review Board (IRB) supervision, 50 abdomen CT scans of were randomly selected from a combination of an ongoing colorectal cancer chemotherapy trial, and a retrospective ventral hernia study. The 50 scans were captured during portal venous contrast phase with variable volume sizes (512 x 512 x 85 - 512 x 512 x 198) and field of views (approx. 280 x 280 x 280 mm3 - 500 x 500 x 650 mm3). The in-plane resolution varies from 0.54 x 0.54 mm2 to 0.98 x 0.98 mm2, while the slice thickness ranges from 2.5 mm to 5.0 mm. The standard registration data was generated by NiftyReg.\r\n\r\nSource: [MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge](https://www.synapse.org/#!Synapse:syn3193805/wiki/217789)\r\n\r\nImage source: [MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge](https://www.synapse.org/#!Synapse:syn3193805/wiki/217789)","description_withheld":null,"homepage":"https://www.synapse.org/#!Synapse:syn3193805/wiki/217789","introduced_date":"2015-04-15","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"}],"languages":[],"variants":["MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge","Synapse multi-organ CT","Synapse"],"data_loaders":[{"repo":"https://github.com/HuCaoFighting/Swin-Unet","url":"https://github.com/HuCaoFighting/Swin-Unet","frameworks":["pytorch"]}],"num_papers_in_archive":31,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-synapse-multi","task":"Medical Image Segmentation","dataset_variant":"Synapse multi-organ CT","rows":23,"metrics":["Avg DSC","Avg HD"],"first_row_in_archive_order":{"model":"Interactive AI-SAM gt box","paper":"/paper/ai-sam-automatic-and-interactive-segment","metrics":{"Avg DSC":"90.66"},"code_links":[{"title":"ymp5078/ai-sam","url":"https://github.com/ymp5078/ai-sam"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-miccai-2015-1","task":"Medical Image Segmentation","dataset_variant":"MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge","rows":8,"metrics":["Avg DSC","Avg HD"],"first_row_in_archive_order":{"model":"MERIT","paper":"/paper/multi-scale-hierarchical-vision-transformer-1","metrics":{"Avg DSC":"84.90","Avg HD":"13.22"},"code_links":[{"title":"SLDGroup/MERIT","url":"https://github.com/SLDGroup/MERIT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-synapse","task":"Medical Image Segmentation","dataset_variant":"Synapse","rows":1,"metrics":["Dice score"],"first_row_in_archive_order":{"model":"nnFormer","paper":"/paper/nnformer-interleaved-transformer-for","metrics":{"Dice score":"0.874"},"code_links":[{"title":"PaddlePaddle/PaddleSeg","url":"https://github.com/PaddlePaddle/PaddleSeg"},{"title":"282857341/nnformer","url":"https://github.com/282857341/nnformer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rwkv-unet-improving-unet-with-long-range","title":"RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation","date":"2025-01-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/s2s2-semantic-stacking-for-robust-semantic","title":"S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging","date":"2024-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/selfreg-unet-self-regularized-unet-for","title":"SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation","date":"2024-06-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":20,"samples_unverified":3,"pointer_only_for_licence":23,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-attention-gated-with-hybrid-dual","title":"Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging","date":"2024-04-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/agileformer-spatially-agile-transformer-unet","title":"AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation","date":"2024-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/paratranscnn-parallelized-transcnn-encoder","title":"ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation","date":"2024-01-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ai-sam-automatic-and-interactive-segment","title":"AI-SAM: Automatic and Interactive Segment Anything Model","date":"2023-12-05","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mist-medical-image-segmentation-transformer","title":"MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder","date":"2023-10-30","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":2,"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/medical-sam-adapter-adapting-segment-anything","title":"Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation","date":"2023-04-25","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/multi-scale-hierarchical-vision-transformer-1","title":"Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation","date":"2023-03-29","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mednext-transformer-driven-scaling-of","title":"MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation","date":"2023-03-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/medsegdiff-v2-diffusion-based-medical-image","title":"MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer","date":"2023-01-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":13,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/medical-image-segmentation-via-cascaded","title":"Medical Image Segmentation via Cascaded Attention Decoding","date":"2023-01-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/improved-abdominal-multi-organ-segmentation","title":"Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks","date":"2022-10-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-t-vmf-dice-loss-for-multi-class","title":"Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation","date":"2022-07-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/missformer-an-effective-medical-image","title":"MISSFormer: An Effective Medical Image Segmentation Transformer","date":"2021-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/uctransnet-rethinking-the-skip-connections-in","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer","date":"2021-09-09","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/nnformer-interleaved-transformer-for","title":"nnFormer: Interleaved Transformer for Volumetric Segmentation","date":"2021-09-07","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/swin-unet-unet-like-pure-transformer-for","title":"Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation","date":"2021-05-12","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":3,"samples_unverified":15,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/transunet-transformers-make-strong-encoders","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","date":"2021-02-08","rows_on_this_dataset":1,"code_links":22,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-semantic-segmentation-from-a","title":"Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers","date":"2020-12-31","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/nnu-net-self-adapting-framework-for-u-net","title":"nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation","date":"2018-09-27","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":3,"samples_unverified":8,"pointer_only_for_licence":0,"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":10,"samples_harvested":97,"samples_ran":57,"samples_unverified":40,"pointer_only_for_licence":42,"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."}