# Automated Design of Deep Learning Methods for Biomedical Image Segmentation

## TL;DR

The paper introduces nnU-Net, an automated deep learning framework for biomedical image segmentation that adapts to various datasets without manual tuning, outperforming specialized methods in numerous competitions.

## Contribution

nnU-Net is a novel, fully automated framework that encodes domain knowledge and adapts deep learning architectures to different biomedical segmentation tasks.

## Key findings

- Outperforms most specialized pipelines in 19 public competitions.
- Sets new state-of-the-art in 49 segmentation tasks.
- Available as open-source tool for broad accessibility.

## Abstract

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.