mlsanityData to 7 Oct 2026

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

arXiv 1809.10486 ↗First posted 27 Sep 2018cs.CVcited by 291

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Abstract (arXiv)

The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation of the U-Net to novel problems, however, comprises several degrees of freedom regarding the exact architecture, preprocessing, training and inference. These choices are not independent of each other and substantially impact the overall performance. The present paper introduces the nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on the basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away superfluous bells and whistles of many proposed network designs and instead focus on the remaining aspects that make out the performance and generalizability of a method. We evaluate the nnU-Net in the context of the Medical Segmentation Decathlon challenge, which measures segmentation performance in ten disciplines comprising distinct entities, image modalities, image geometries and dataset sizes, with no manual adjustments between datasets allowed. At the time of manuscript submission, nnU-Net achieves the highest mean dice scores across all classes and seven phase 1 tasks (except class 1 in BrainTumour) in the online leaderboard of the challenge.

Key facts. Cited by 291 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 47; 2022: 56; 2023: 45; 2024: 54; 2025: 57; 2026: 32.

291 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
  3. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
  4. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  5. 2024Exploring scalable medical image encoders beyond text supervisioncited by 129
  6. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  7. 2021Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationcited by 88
  8. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
  9. 2023SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Modelcited by 61
  10. 2021Positional Contrastive Learning for Volumetric Medical Image Segmentationcited by 56
  11. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  12. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
38 more
  1. 2024Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decodingcited by 48
  2. 2021Efficient Medical Image Segmentation Based on Knowledge Distillationcited by 43
  3. 2024Visual Mamba: A Survey and New Outlookscited by 41
  4. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  5. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  6. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  7. 2023Loss Functions in the Era of Semantic Segmentation: A Survey and Outlookcited by 33
  8. 2023SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Modelcited by 32
  9. 2024SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentationcited by 31
  10. 2023A Unified Framework for U-Net Design and Analysiscited by 27
  11. 2021Deep Implicit Statistical Shape Models for 3D Medical Image Delineationcited by 26
  12. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  13. 2022ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classificationcited by 25
  14. 2025MedDINOv3: How to adapt vision foundation models for medical image segmentation?cited by 25
  15. 2022Q-Net: Query-Informed Few-Shot Medical Image Segmentationcited by 24
  16. 2021A persistent homology-based topological loss for CNN-based multi-class segmentation of CMRcited by 21
  17. 2024Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentationcited by 20
  18. 2021BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentationcited by 19
  19. 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Futurecited by 18
  20. 2021A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentationcited by 17
  21. 2021Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Losscited by 16
  22. 2023Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localizationcited by 16
  23. 2021Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learningcited by 15
  24. 2022CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRIcited by 15
  25. 2024UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentationcited by 15
  26. 2021Towards Robust General Medical Image Segmentationcited by 14
  27. 2021ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissectioncited by 14
  28. 2022SFusion: Self-attention based N-to-One Multimodal Fusion Blockcited by 14
  29. 2022When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentationcited by 13
  30. 2022Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentationcited by 13
  31. 2023M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentationcited by 13
  32. 2023Boundary Difference Over Union Loss For Medical Image Segmentationcited by 13
  33. 2024DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentationcited by 13
  34. 2021Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentationcited by 12
  35. 2021MONAIfbs: MONAI-based fetal brain MRI deep learning segmentationcited by 12
  36. 2022Lymphoma segmentation from 3D PET-CT images using a deep evidential networkcited by 12
  37. 2022FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentationcited by 12
  38. 2022Evidence fusion with contextual discounting for multi-modality medical image segmentationcited by 12

50 most cited of 291 All 291 (JSON)