LesionMix: A Lesion-Level Data Augmentation Method for Medical Image
  Segmentation
Berke Doga Basaran Weitong Zhang Mengyun Qiao Bernhard Kainz Paul M. Matthews Wenjia Bai
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.