论文标题
脑肿瘤分割，自我振兴，深度监督3D U-NET神经网络：BRATS 2020挑战解决方案
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
论文作者
论文摘要
脑肿瘤细分是患者疾病管理的关键任务。为了使这项任务自动化和标准化，我们在多模式的脑肿瘤分割挑战（BRATS）2020培训数据集上训练了多个U-NET（例如神经网络），主要是通过深度监督和随机重量平均。训练了来自两个不同训练管道的两个独立组合，并产生了脑肿瘤分割图。然后考虑到特定肿瘤子区域的每个集合的性能，将每个患者的这两个标签图合并。我们在通过测试时间增加的在线验证数据集上的性能如下：骰子为0.81、0.91和0.85；分别用于增强肿瘤，整个肿瘤和肿瘤核心的Hausdorff（95％）为20.6、4,3、5.7毫米。同样，我们的解决方案在最终测试数据集中获得了0.79、0.89和0.84的骰子，以及20.4、6.7和19.5mm的Hausdorff（95％）的骰子，将我们排在前十支球队中。研究了更复杂的培训方案和神经网络体系结构，而没有大幅增加培训时间，而没有大幅度的绩效提高。总体而言，我们的方法为每个肿瘤子区域提供了良好的平衡性能。我们的解决方案在https://github.com/lescientifik/open_brats2020上开放。
Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4,3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mm on the final test dataset, ranking us among the top ten teams. More complicated training schemes and neural network architectures were investigated without significant performance gain at the cost of greatly increased training time. Overall, our approach yielded good and balanced performance for each tumor subregion. Our solution is open sourced at https://github.com/lescientifik/open_brats2020.
