# Extending nnU-Net is all you need

arXiv: https://arxiv.org/abs/2208.10791 · HTML: https://mlsanity.com/paper/2208.10791/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI](https://mlsanity.com/paper/2011.07592/) (2020)
- [Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?](https://mlsanity.com/paper/2101.00232/) (2021)

## Built on by (most cited first, 22 of 22)

- [MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation](https://mlsanity.com/paper/2303.14444/) (2023)
- [Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?](https://mlsanity.com/paper/2411.03670/) (2024)
- [E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2312.04727/) (2023)
- [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025)
- [SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation](https://mlsanity.com/paper/2402.16368/) (2024)
- [Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head](https://mlsanity.com/paper/2309.17143/) (2023)
- [RadioActive: 3D Radiological Interactive Segmentation Benchmark](https://mlsanity.com/paper/2411.07885/) (2024)
- [DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks](https://mlsanity.com/paper/2502.15128/) (2025)
- [Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans](https://mlsanity.com/paper/2309.13747/) (2023)
- [LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas](https://mlsanity.com/paper/2406.03984/) (2024)
- [RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement](https://mlsanity.com/paper/2309.07513/) (2023)
- [Mitigating False Predictions In Unreasonable Body Regions](https://mlsanity.com/paper/2404.15718/) (2024)
- [AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model Ensemble](https://mlsanity.com/paper/2409.13779/) (2024)
- [A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks](https://mlsanity.com/paper/2506.04121/) (2025)
- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation](https://mlsanity.com/paper/2403.10216/) (2024)
- [DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction](https://mlsanity.com/paper/2404.08108/) (2024)
- [Data-Centric Label Smoothing for Explainable Glaucoma Screening from Eye Fundus Images](https://mlsanity.com/paper/2406.03903/) (2024)
- [Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation](https://mlsanity.com/paper/2504.06741/) (2025)
- [SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation](https://mlsanity.com/paper/2603.22002/) (2026)
- [Contour-Guided Query-Based Feature Fusion for Boundary-Aware and Generalizable Cardiac Ultrasound Segmentation](https://mlsanity.com/paper/2603.28110/) (2026)
- [Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It](https://mlsanity.com/paper/2604.02564/) (2026)

## Abstract (arXiv)

Semantic segmentation is one of the most popular research areas in medical image computing. Perhaps surprisingly, despite its conceptualization dating back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions for a broad variety of segmentation problems and is regularly used as a development framework for challenge-winning algorithms. Here we use nnU-Net to participate in the AMOS2022 challenge, which comes with a unique set of tasks: not only is the dataset one of the largest ever created and boasts 15 target structures, but the competition also requires submitted solutions to handle both MRI and CT scans. Through careful modification of nnU-net's hyperparameters, the addition of residual connections in the encoder and the design of a custom postprocessing strategy, we were able to substantially improve upon the nnU-Net baseline. Our final ensemble achieves Dice scores of 90.13 for Task 1 (CT) and 89.06 for Task 2 (CT+MRI) in a 5-fold cross-validation on the provided training cases.