Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder

Abstract

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 selfsupervised 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.

Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

“…Other studies suggest the use of VAEs to improve the segmentation task performance. Huo et al [83] introduce a progressive VAE-based architecture (PAVAE) for generating synthetic brain lesions with associated segmentation masks. The authors propose a twostep pipeline where the first step consists in generating synthetic segmentation masks based on a conditional adversarial VAE.…”

“…Furthermore, VAEs are often used in hybrid architectures with adversarial learning techniques. The most promising architectures include PAVAE [83] and IntroVAE [122], alongside conditional VAEs, for various purposes including classification, segmentation, and translation tasks. However, while VAEs have shown potential in these areas, there is still room for improvement.…”