论文标题
使用gan的病变掩模的同时合成解剖和分子图像
Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN
论文作者
论文摘要
数据驱动的自动方法已经证明了它们在神经肿瘤学中恶性神经胶质瘤患者借助常规和晚期分子MR图像的神经肿瘤患者解决各种临床诊断困境的巨大潜力。但是，缺乏足够的注释MRI数据极大地阻碍了这种自动方法的发展。传统的数据增强方法，包括翻转，缩放，旋转和失真，无法生成具有不同图像内容的数据。 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 (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), fluid-attenuated反转恢复（FLAIR）和酰胺质子转移加权（APTW）。提出的框架包括一个伸展的上采样模块，一个大脑图集编码器，分段一致性模块和多尺度标签的歧视器。关于实际临床数据的广泛实验表明，所提出的模型可以比最先进的合成方法更好。
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 (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and amide proton transfer-weighted (APTw). 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.
