LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
This work addresses the need for better data augmentation in medical imaging for lesion segmentation, offering a domain-specific improvement that is incremental but practical.
The paper tackles the problem of limited lesion diversity in medical image segmentation by introducing LesionMix, a lesion-level data augmentation method that increases shape, location, intensity, and load distribution variations, resulting in improved performance over other Mix-based methods across multiple datasets.
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 https://github.com/dogabasaran/lesionmix.