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A large annotated medical image dataset for the development and evaluation of segmentation algorithms

arXiv 1902.09063 ↗First posted 25 Feb 2019cs.CV eess.IVcited by 371

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Abstract (arXiv)

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.

Key facts. Cited by 371 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2019.

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Citing papers per year: 2021: 82; 2022: 71; 2023: 75; 2024: 63; 2025: 56; 2026: 24.

371 papers.

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  1. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  2. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  3. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  4. 2021The Medical Segmentation Decathloncited by 620
  5. 2021SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answeringcited by 444
  6. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  7. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  8. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  9. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  10. 2022Transformers in Medical Imaging: A Surveycited by 245
  11. 2022Metrics reloaded: Recommendations for image analysis validationcited by 190
  12. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
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  1. 2024M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Modelscited by 154
  2. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  3. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
  4. 2021Medical Visual Question Answering: A Surveycited by 93
  5. 2023UniverSeg: Universal Medical Image Segmentationcited by 90
  6. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 86
  7. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  8. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  9. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 71
  10. 2021Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learningcited by 68
  11. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  12. 2021nnDetection: A Self-configuring Method for Medical Object Detectioncited by 60
  13. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  14. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  15. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  16. 2021AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentationcited by 48
  17. 2021DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentationcited by 47
  18. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  19. 2021CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomographycited by 45
  20. 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 44
  21. 2021MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentationcited by 43
  22. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  23. 2023CLIP in Medical Imaging: A Surveycited by 40
  24. 2023Segment Anything Model for Medical Images?cited by 39
  25. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  26. 2023One-Prompt to Segment All Medical Imagescited by 38
  27. 2021How I failed machine learning in medical imaging -- shortcomings and recommendationscited by 37
  28. 2023SAM3D: Segment Anything Model in Volumetric Medical Imagescited by 34
  29. 2024Revisiting MAE pre-training for 3D medical image segmentationcited by 33
  30. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  31. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  32. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  33. 2023Robustness of SAM: Segment Anything Under Corruptions and Beyondcited by 30
  34. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  35. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  36. 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
  37. 2023CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolutioncited by 28
  38. 2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28

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