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.


翻译:U-Net是2015年推出的,其直向和成功的架构迅速演变为医疗图像分割中常用的基准。但是,U-Net适应新问题,在精确结构、预处理、培训和推理方面,它包含若干程度的自由度,这些选择互不独立,对总体绩效产生很大影响。本文件介绍nnU-Net(“不新网”),它是指基于 2D 和 3D Vanilla U-Net 的稳健和自我调整框架。在提交手写稿时,NN-Net在所有级别和7 阶段任务中实现最高平均值的在线领导人排名。

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