mlsanityData to 7 Oct 2026

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

arXiv 2107.02314 ↗First posted 5 Jul 2021cs.CVcited by 507

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Cited by 57 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of 60,000 collectively.

Key facts. Cited by 507 papers in the map (data through 7 Oct 2026). Builds on 5 of its 58 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

5 of 58 references.

References · 5

  1. 2020The Future of Digital Health with Federated Learningcited by 625
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  3. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  4. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  5. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 37; 2023: 85; 2024: 130; 2025: 134; 2026: 106.

507 papers.

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  1. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  2. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  3. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  4. 2022Transformers in Medical Imaging: A Surveycited by 245
  5. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
  6. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Modelcited by 180
  7. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  8. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 161
  9. 2024Medical SAM 2: Segment medical images as video via Segment Anything Model 2cited by 151
  10. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  11. 2022SimpleClick: Interactive Image Segmentation with Simple Vision Transformerscited by 109
  12. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 101
38 more
  1. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  2. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
  3. 2023UniverSeg: Universal Medical Image Segmentationcited by 90
  4. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  5. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  6. 2023Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosiscited by 77
  7. 2023SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anythingcited by 75
  8. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  9. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 65
  10. 2023Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Studycited by 64
  11. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 60
  12. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  13. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  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. 2023The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningiomacited by 51
  17. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)cited by 51
  18. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)cited by 49
  19. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  20. 2023UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learnercited by 46
  21. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  22. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  23. 2022PseudoClick: Interactive Image Segmentation with Click Imitationcited by 42
  24. 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
  25. 2022One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputationcited by 40
  26. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  27. 2023One-Prompt to Segment All Medical Imagescited by 38
  28. 2021Extending nn-UNet for brain tumor segmentationcited by 36
  29. 2024BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentationcited by 36
  30. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  31. 2024Classifier-guided Gradient Modulation for Enhanced Multimodal Learningcited by 34
  32. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  33. 2021The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patientscited by 32
  34. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  35. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  36. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  37. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  38. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Imagescited by 30

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