CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

• URL: http://arxiv.org/abs/2108.06883v2
• Date: Tue, 17 Aug 2021 02:04:46 GMT
• Title: CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation
• Authors: Xinru Zhang, Chenghao Liu, Ni Ou, Xiangzhu Zeng, Xiaoliang Xiong, Yizhou Yu, Zhiwen Liu, Chuyang Ye
• Abstract summary: We propose a simple data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Our method improves the segmentation accuracy compared with other simple data augmentation approaches.
• Score: 38.785081940984135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other "mix"-based methods, such as Mixup and CutMix, CarveMix stochastically combines two existing labeled images to generate new labeled samples. Yet, unlike these augmentation strategies based on image combination, CarveMix is lesion-aware, where the combination is performed with an attention on the lesions and a proper annotation is created for the generated image. Specifically, from one labeled image we carve a region of interest (ROI) according to the lesion location and geometry, and the size of the ROI is sampled from a probability distribution. The carved ROI then replaces the corresponding voxels in a second labeled image, and the annotation of the second image is replaced accordingly as well. In this way, we generate new labeled images for network training and the lesion information is preserved. To evaluate the proposed method, experiments were performed on two brain lesion datasets. The results show that our method improves the segmentation accuracy compared with other simple data augmentation approaches.

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