mlsanityData to 7 Oct 2026

AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

arXiv 2206.08023 ↗First posted 16 Jun 2022eess.IV cs.CV cs.LGcited by 347

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Abstract (arXiv)

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. Information can be found at https://amos22.grand-challenge.org.

Key facts. Cited by 347 papers in the map (data through 7 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

21 of 36 references.

References · 21

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  5. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  6. 2021The Medical Segmentation Decathloncited by 620
  7. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  8. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  9. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
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9 more
  1. 2018Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapycited by 76
  2. 2021STEP: Segmenting and Tracking Every Pixelcited by 72
  3. 2020Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstractioncited by 70
  4. 2019C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentationcited by 49
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Citing papers per year: 2022: 9; 2023: 64; 2024: 96; 2025: 118; 2026: 60.

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  1. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  2. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  3. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  4. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  5. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  6. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 161
  7. 2024M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Modelscited by 154
  8. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  9. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 101
  10. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  11. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  12. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
38 more
  1. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  2. 2023UniverSeg: Universal Medical Image Segmentationcited by 90
  3. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 86
  4. 2023MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentationcited by 82
  5. 20233DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationcited by 70
  6. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  7. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 62
  8. 2023SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Modelcited by 61
  9. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  10. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  11. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 53
  12. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  13. 2023Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentationcited by 51
  14. 2023Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challengecited by 50
  15. 2024Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decodingcited by 48
  16. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  17. 2023MultiTalent: A Multi-Dataset Approach to Medical Image Segmentationcited by 45
  18. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  19. 2023Segment Anything Model for Medical Images?cited by 39
  20. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  21. 2023One-Prompt to Segment All Medical Imagescited by 38
  22. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentationcited by 34
  23. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  24. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 34
  25. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 32
  26. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  27. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  28. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  29. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  30. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  31. 2024Learning General-Purpose Biomedical Volume Representations using Randomized Synthesiscited by 27
  32. 2024M4oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Expertscited by 26
  33. 2023DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentationcited by 25
  34. 2025MedDINOv3: How to adapt vision foundation models for medical image segmentation?cited by 25
  35. 2024An OpenMind for 3D medical vision self-supervised learningcited by 23
  36. 2022Extending nnU-Net is all you needcited by 22
  37. 2022MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recoverycited by 22
  38. 2023I-MedSAM: Implicit Medical Image Segmentation with Segment Anythingcited by 22

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