论文标题
3D脑肿瘤分割的最佳数据增强是什么？
What is the best data augmentation for 3D brain tumor segmentation?
论文作者
论文摘要
培训细分网络需要大量注释的数据集，在医学成像中很难获得。尽管这一事实，我们认为并未完全探索数据扩展以进行脑肿瘤细分。在该项目中，我们在训练标准3D U-NET时应用不同类型的数据增强（翻转，旋转，缩放，亮度调整，弹性变形），并证明在许多情况下，增强可显着提高网络的性能。我们的结论是，亮度增强和弹性变形最有效，与仅使用一种增强技术相比，不同增强技术的组合并不能提供进一步的改进。我们的代码可从https://github.com/mdciri/3d-agmentation-techniques获得。
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been fully explored for brain tumor segmentation. In this project we apply different types of data augmentation (flipping, rotation, scaling, brightness adjustment, elastic deformation) when training a standard 3D U-Net, and demonstrate that augmentation significantly improves the network's performance in many cases. Our conclusion is that brightness augmentation and elastic deformation work best, and that combinations of different augmentation techniques do not provide further improvement compared to only using one augmentation technique. Our code is available at https://github.com/mdciri/3D-augmentation-techniques.
