This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available.

翻译：本文提出了一种有效且通用的医学图像分割数据增强框架。我们采用计算高效且数据高效的基于梯度的元学习方案，明确对齐训练数据与用作未见测试数据代理的验证数据的分布。我们通过两个核心设计改进了当前的数据增强策略。首先，我们学习类别特定的训练时数据增强（TRA），有效增加训练子集内的异质性，并解决分割中常见的类别不平衡问题。其次，我们联合优化TRA与测试时数据增强（TEA），两者紧密相关，均旨在对齐训练与测试数据分布，但在以往工作中被分别考虑。我们在两个最先进的分割模型DeepMedic和nnU-Net上，针对不同场景下的四项医学图像分割任务证明了方法的有效性。大量实验表明，与现有解决方案相比，所提出的数据增强框架能够显著且一致地提升分割性能。代码已公开。