论文标题
主体特异性病变的产生和伪健康合成多发性硬化症脑图像
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
论文作者
论文摘要
了解脑病变的强度特征是定义神经系统研究和预测疾病负担和预后的基于图像的生物标志物的关键。在这项工作中，我们提出了一种基于前景的新型生成方法，用于对局部病变特征进行建模，该方法既可以在健康图像上产生合成病变，又可以从病理图像中综合受试者特异性的伪健康图像。此外，提出的方法可以用作数据增强模块，以生成用于训练大脑图像分割网络的合成图像。在磁共振成像（MRI）上获得的多发性硬化症（MS）脑图像的实验表明，该提出的方法可以产生高度逼真的伪卫生和伪病理学大脑图像。与传统的数据增强方法以及最近感知的数据增强技术Carvemix相比，使用合成图像进行数据增强可改善大脑图像分割性能。该代码将在https://github.com/dogabasaran/lesion-synthesis中发布。
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.
