论文标题
NNU-NET用于脑肿瘤分割
nnU-Net for Brain Tumor Segmentation
论文作者
论文摘要
我们将NNU-NET应用于Brats 2020挑战的细分任务。未修改的NNU-NET基线配置已经取得了可观的结果。通过合并针对后处理，基于区域的培训，更具侵略性的数据增强以及对NNUNET管道的几次较小修改的特定修改，我们可以大大改善其细分性能。此外，我们重新实现Brats排名方案，以确定哪种NNU-NET变体最适合其所征得的要求。我们的最后一部合奏在2020年Brats比赛中排名第一，分别为88.95、85.06和82.03和HD95值分别为8.498,17.337和17.805的HD95值分别为整个肿瘤，肿瘤核心和增强肿瘤。
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.
