脑部病变分割为临床诊断和研究提供了一个宝贵的工具，卷积神经网络 (CNN) 在分割任务中取得了前所未有的成功。数据增强是提高 CNN 训练效果的一种广泛使用的策略。特别地，一些将已注释的训练图像对混合的数据增强方法已得到开发，这些方法易于实现，并在各种影像处理任务中取得了有希望的结果。但是，现有基于图像混合的数据增强方法并非为脑部病变设计，不一定适用于脑部病变分割。因此，设计这种简单的脑部病变分割数据增强方法仍然是一个未解决的问题。在这项工作中，我们提出了一种简单而有效的数据增强方法，被称为CarveMix，用于基于 CNN 的脑部病变分割。像其他混合方法一样，CarveMix 随机地将两个已有的、仅针对脑部病变的注释图像组合起来，以获得新的标注样本。为了使我们的方法更适合脑部病变分割，CarveMix 是基于病变感知的，图像组合是以病变为重点进行的，保留了病变信息。具体来说，我们根据病变的位置和几何形状从一个注释图像中雕刻出感兴趣的区域 (ROI) ，其大小可变。然后，将雕刻的 ROI 替换成第二个注释图像中对应的体素，以合成用于网络训练的新的标注图像，并在两个已注释图像可能来自不同来源的异构数据上进行额外的协调步骤。此外，我们进一步提出了在图像混合期间对整个脑部肿瘤分割独特的占位效应进行建模。为了评估所提出的方法，我们在多个公开或私人数据集上进行了实验，结果显示我们的方法提高了脑部病变分割的准确性。所提出方法的代码可在 https://github.com/ZhangxinruBIT/CarveMix.git 获取。版权所有 © 2023 作者。由 Elsevier Inc. 发布，版权所有。

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.