Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on multiple sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI) demonstrate that the proposed method can generate highly realistic pseudo-healthy and pseudo-pathological brain images. Data augmentation using the synthetic images improves the brain image segmentation performance compared to traditional data augmentation methods as well as a recent lesion-aware data augmentation technique, CarveMix. The code will be released at https://github.com/dogabasaran/lesion-synthesis.

翻译：了解脑损伤强度特征是确定神经研究中基于图像的生物标志以及预测疾病负担和结果的关键。在这项工作中,我们提出了一个基于地表的新型基因化方法,用于模拟局部损伤特征,既能对健康图像产生合成损害,又能综合病理图像中针对特定主题的假健康图像。此外,拟议方法可以用作数据增强模块,生成合成图像,用于培训大脑图像分割网络。磁共振成像(MRI)中获取的多颗硬化(MS)脑图像实验表明,拟议方法能够产生非常现实的假体健康和伪病理大脑图像。使用合成图像进行的数据增强提高了大脑图像的分化性能,与传统数据增强方法相比,以及最近的“有色分变数据增强技术”CarveMix。该代码将在https://github.com/dogabasaran/lesion-synthes发布。