Data augmentation has become a de facto component of deep learning-based
medical image segmentation methods. Most data augmentation techniques used in
medical imaging focus on spatial and intensity transformations to improve the
diversity of training images. They are often designed at the image level,
augmenting the full image, and do not pay attention to specific abnormalities
within the image. Here, we present LesionMix, a novel and simple lesion-aware
data augmentation method. It performs augmentation at the lesion level,
increasing the diversity of lesion shape, location, intensity and load
distribution, and allowing both lesion populating and inpainting. Experiments
on different modalities and different lesion datasets, including four brain MR
lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix
achieves promising performance in lesion image segmentation, outperforming
several recent Mix-based data augmentation methods. The code will be released
at this https URL

LesionMix 是一种新颖而简单的病变感知数据增强方法，在病变层面上进行增强，增加病变的形状、位置、强度和负载分布的多样性，并允许进行病变填充和修复，在多种模态和病变数据集上表现出有希望的病变图像分割性能，优于几种最近的基于混合的数据增强方法。