Accurate brain lesion delineation is important for planning neurosurgical
treatment. Automatic brain lesion segmentation methods based on convolutional
neural networks have demonstrated remarkable performance. However, neural
network performance is constrained by the lack of large-scale well-annotated
training datasets. In this manuscript, we propose a comprehensive framework to
efficiently generate new, realistic samples for training a brain lesion
segmentation model. We first train a lesion generator, based on an adversarial
autoencoder, in a self-supervised manner. Next, we utilize a novel image
composition algorithm, Soft Poisson Blending, to seamlessly combine synthetic
lesions and brain images to obtain training samples. Finally, to effectively
train the brain lesion segmentation model with augmented images we introduce a
new prototype consistence regularization to align real and synthetic features.
Our framework is validated by extensive experiments on two public brain lesion
segmentation datasets: ATLAS v2.0 and Shift MS. Our method outperforms existing
brain image data augmentation schemes. For instance, our method improves the
Dice from 50.36% to 60.23% compared to the U-Net with conventional data
augmentation techniques for the ATLAS v2.0 dataset.

提出了一个综合框架，通过基于对抗自编码器的病变生成器和软泊松融合算法，高效地生成用于训练脑部病变分割模型的新的逼真样本，并引入原型一致性正则化以实现真实和合成特征的对齐，验证表明该方法在两个公共脑部病变分割数据集上优于现有的数据增强方案。