Guo Pengfei, Wang Puyang, Zhou Jinyuan, Patel Vishal M, Jiang Shanshan
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Med Image Comput Comput Assist Interv. 2020 Oct;12262:104-113. doi: 10.1007/978-3-030-59713-9_11. Epub 2020 Sep 29.
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional and advanced molecular MR images. However, the lack of sufficient annotated MRI data has vastly impeded the development of such automatic methods. Conventional data augmentation approaches, including flipping, scaling, rotation, and distortion are not capable of generating data with diverse image content. In this paper, we propose a method, called synthesis of anatomic and molecular MR images network (SAMR), which can simultaneously synthesize data from arbitrary manipulated lesion information on multiple anatomic and molecular MRI sequences, including T1-weighted ( w), gadolinium enhanced w (Gd- w), T2-weighted ( w), fluid-attenuated inversion recovery (), and amide proton transfer-weighted (w). The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators. Extensive experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
数据驱动的自动方法已借助传统和先进的分子磁共振成像（MR）图像，在解决神经肿瘤学中恶性胶质瘤患者的各种临床诊断难题方面展现出巨大潜力。然而，缺乏足够的标注MRI数据极大地阻碍了此类自动方法的发展。传统的数据增强方法，包括翻转、缩放、旋转和扭曲，无法生成具有多样图像内容的数据。在本文中，我们提出了一种名为解剖和分子MR图像合成网络（SAMR）的方法，它可以同时从多个解剖和分子MRI序列上的任意操纵病变信息合成数据，包括T1加权（T1w）、钆增强T1w（Gd-T1w）、T2加权（T2w）、液体衰减反转恢复（FLAIR）和酰胺质子转移加权（APTw）。所提出的框架由一个展开式上采样模块、一个脑图谱编码器、一个分割一致性模块和多尺度标签判别器组成。对真实临床数据进行的大量实验表明，所提出的模型在性能上显著优于当前最先进的合成方法。
