# Automated Design of Deep Learning Methods for Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/1904.08128 · HTML: https://mlsanity.com/paper/1904.08128/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 27 of 27)

- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data](https://mlsanity.com/paper/2203.04419/) (2022)
- [blob loss: instance imbalance aware loss functions for semantic segmentation](https://mlsanity.com/paper/2205.08209/) (2022)
- [4D Myocardium Reconstruction with Decoupled Motion and Shape Model](https://mlsanity.com/paper/2308.14083/) (2023)
- [Continual Hippocampus Segmentation with Transformers](https://mlsanity.com/paper/2204.08043/) (2022)
- [Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge](https://mlsanity.com/paper/2110.01607/) (2021)
- [Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey](https://mlsanity.com/paper/2206.07171/) (2022)
- [SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection](https://mlsanity.com/paper/2110.03828/) (2021)
- [Landmarks Augmentation with Manifold-Barycentric Oversampling](https://mlsanity.com/paper/2104.00925/) (2021)
- [DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI](https://mlsanity.com/paper/2106.03388/) (2021)
- [Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19](https://mlsanity.com/paper/2211.05548/) (2022)
- [Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions](https://mlsanity.com/paper/2203.10507/) (2022)
- [Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation](https://mlsanity.com/paper/2209.09678/) (2022)
- [Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation](https://mlsanity.com/paper/2408.04610/) (2024)
- [RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy](https://mlsanity.com/paper/2106.11942/) (2021)
- [High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm](https://mlsanity.com/paper/2107.10746/) (2021)
- [HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation](https://mlsanity.com/paper/2311.12461/) (2023)
- [Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation](https://mlsanity.com/paper/2405.03420/) (2024)
- [SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation](https://mlsanity.com/paper/2109.03435/) (2021)
- [Comprehensive study of good model training for prostate segmentation in volumetric MRI](https://mlsanity.com/paper/2208.13671/) (2022)
- [The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data](https://mlsanity.com/paper/2306.10484/) (2023)
- [Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets](https://mlsanity.com/paper/2403.19177/) (2024)
- [Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers](https://mlsanity.com/paper/2405.12781/) (2024)
- [Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach](https://mlsanity.com/paper/2509.06511/) (2025)

## Abstract (arXiv)

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.