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.

利用人工智能中的深度学习技术，基于 nnU-Net 框架实现了高质量的三维生物医学图像分割和量化，该框架不需要人工参与，能够自动适应不同的数据集和分割任务，同时在 19 项公共比赛中取得了优于大部分深度学习算法的表现。