We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor, respectively.

本文通过对 nnU-Net 模型进行 BraTS 2020 挑战的分割任务，在后处理、基于区域的训练、更激进的数据增强以及对 nnUNet 管道的一些小修改方面，结合 BraTS 特定的修改，大大提高了其分割性能。在重新实现 BraTS 排名方案以确定我们的 nnU-Net 变体中最符合其要求的方案的同时，我们的最终集成在整个肿瘤中，肿瘤核心和增强肿瘤方面的 Dice 分数分别为 88.95、85.06 和 82.03，HD95 值分别为 8.498、17.337 和 17.805 获得了 BraTS 2020 比赛的第一名。

nnU-Net用于脑肿瘤分割