The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also requires data augmentation to extend diversity for preventing overfitting. However, the existing methods for data augmentation of medical image segmentation are mainly based on models which need to update parameters and cost extra computing resources. We proposed data augmentation methods designed to train a high accuracy deep learning network for medical image segmentation. The proposed data augmentation approaches are called KeepMask and KeepMix, which can create medical images by better identifying the boundary of the organ with no more parameters. Our methods achieved better performance and obtained more precise boundaries for medical image segmentation on datasets. The dice coefficient of our methods achieved 94.15% (3.04% higher than baseline) on CHAOS and 74.70% (5.25% higher than baseline) on MSD spleen with Unet.

翻译：利用深度学习进行医学图像分割的发展可显著辅助医生诊断。深度学习训练需要大量数据，同时需要数据增强以扩展多样性、防止过拟合。然而，现有医学图像分割的数据增强方法主要基于需要更新参数且消耗额外计算资源的模型。我们提出了专为训练高精度医学图像分割深度学习网络而设计的数据增强方法。所提出的数据增强方法称为KeepMask和KeepMix，其无需额外参数即可通过更准确地识别器官边界来生成医学图像。我们的方法在数据集上实现了更优的医学图像分割性能，并获得了更精确的边界。在CHAOS数据集上，我们的方法结合Unet的dice系数达到94.15%（较基线提升3.04%），在MSD脾脏数据集上达到74.70%（较基线提升5.25%）。