论文标题
具有肿瘤一致性损失的多模式生成对抗网络MR图像合成
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis
论文作者
论文摘要
不同模态的磁共振（MR）图像可以提供互补的信息以进行临床诊断，但是整个模式通常是昂贵的。大多数现有方法仅着眼于综合两种方式之间缺少的图像，这在丢失多种方式时限制了它们的鲁棒性和效率。为了解决这个问题，我们提出了一个多模式生成对抗网络（MGAN），以同时从一个MR模态T2同时合成三种高质量的MR模式（Flair，T1和T1CE）。实验结果表明，通过我们提出的方法，合成图像的质量比基线模型Pix2Pix合成的方法更好。此外，对于MR Brain图像合成，保留生成的模态中的关键肿瘤信息很重要，因此我们进一步向称为TC-MGAN的MGAN引入了多模式肿瘤的一致性损失。我们使用TC-MGAN使用合成的方式来提高肿瘤分割的精度，结果证明了其有效性。
Magnetic Resonance (MR) images of different modalities can provide complementary information for clinical diagnosis, but whole modalities are often costly to access. Most existing methods only focus on synthesizing missing images between two modalities, which limits their robustness and efficiency when multiple modalities are missing. To address this problem, we propose a multi-modality generative adversarial network (MGAN) to synthesize three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2 simultaneously. The experimental results show that the quality of the synthesized images by our proposed methods is better than the one synthesized by the baseline model, pix2pix. Besides, for MR brain image synthesis, it is important to preserve the critical tumor information in the generated modalities, so we further introduce a multi-modality tumor consistency loss to MGAN, called TC-MGAN. We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.
