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nnU-Net for Brain Tumor Segmentation

arXiv 2011.00848 ↗First posted 2 Nov 2020eess.IV cs.CVcited by 103

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Abstract (arXiv)

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor, respectively.

Key facts. Cited by 103 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Nov 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 16; 2023: 20; 2024: 23; 2025: 15; 2026: 14.

103 papers.

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  1. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  2. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 419
  3. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  4. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  5. 2021A Robust Volumetric Transformer for Accurate 3D Tumor Segmentationcited by 41
  6. 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
  7. 2021Extending nn-UNet for brain tumor segmentationcited by 36
  8. 2024How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentationcited by 28
  9. 2021Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficientcited by 26
  10. 2023Neuralizer: General Neuroimage Analysis without Re-Trainingcited by 24
  11. 2022Extending nnU-Net is all you needcited by 22
  12. 2022Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentationcited by 21
38 more
  1. 2022Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solutioncited by 17
  2. 2021Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentationcited by 16
  3. 2021Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIscited by 15
  4. 2023The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planningcited by 15
  5. 2021Towards Robust General Medical Image Segmentationcited by 14
  6. 2023A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentationcited by 13
  7. 2023Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation mapscited by 12
  8. 2024Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Taskscited by 12
  9. 2022Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentationcited by 11
  10. 2024IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRIcited by 9
  11. 2025Regression is all you need for medical image translationcited by 8
  12. 2021Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?cited by 7
  13. 2021Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challengecited by 7
  14. 2022Brain tumour segmentation with incomplete imaging datacited by 7
  15. 2022Large-Kernel Attention for 3D Medical Image Segmentationcited by 7
  16. 2024LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentationcited by 7
  17. 2023Scale-Equivariant Deep Learning for 3D Datacited by 6
  18. 2023Hybrid-Fusion Transformer for Multisequence MRIcited by 6
  19. 2024MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Frameworkcited by 5
  20. 2024Brain Tumour Removing and Missing Modality Generation using 3D WDMcited by 4
  21. 2024Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentationcited by 4
  22. 2024Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatricscited by 4
  23. 2025Constitution or Collapse? Exploring Constitutional AI with Llama 3-8Bcited by 4
  24. 2021Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Featurescited by 3
  25. 2022Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patientscited by 3
  26. 2023Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentationcited by 3
  27. 2024MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structurescited by 3
  28. 2024Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selectioncited by 3
  29. 2024Interactive Image Selection and Training for Brain Tumor Segmentation Networkcited by 3
  30. 2024Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysiscited by 3
  31. 2026The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT unicodex2013 Multitracer Multicenter Generalizationcited by 3
  32. 2021Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentationcited by 2
  33. 2021Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretrainingcited by 2
  34. 2021Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentationcited by 2
  35. 2021Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRIcited by 2
  36. 2022Negligible effect of brain MRI data preprocessing for tumor segmentationcited by 2
  37. 2023FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentationcited by 2
  38. 2023Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON planscited by 2

50 most cited of 103 All 103 (JSON)