DC-cycleGAN：基于非配对数据的双向CT到MR合成
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
AI总结：
本文提出DC-cycleGAN，通过双对比损失并结合交叉熵与SSIM，在非配对数据上实现CT与MR的双向医学图像合成，实验表明其性能优于多种cycleGAN类方法。
摘要：
磁共振（MR）和计算机断层扫描（CT）图像是两种典型的医学图像，可为准确的临床诊断和治疗提供互补信息。然而，由于成本、辐射剂量和模态缺失等考虑，同时获取这两种图像可能受到限制。近年来，医学图像合成引起了越来越多的研究兴趣，以应对这一限制。本文提出了一种双向学习模型，称为双对比循环GAN（DC-cycleGAN），用于从非配对数据合成医学图像。具体而言，在判别器中引入双对比损失，利用源域样本作为负样本，间接在真实源图像与合成图像之间建立约束，并迫使合成图像远离源域。此外，将交叉熵和结构相似性指数（SSIM）集成到DC-cycleGAN中，以便在合成图像时同时考虑样本的亮度和结构。实验结果表明，与其他基于cycleGAN的医学图像合成方法（如cycleGAN、RegGAN、DualGAN和NiceGAN）相比，DC-cycleGAN能够产生有前景的结果。代码将在 https://github.com/JiayuanWang-JW/DC-cycleGAN 提供。
英文摘要：
Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aroused gaining research interest to cope with this limitation. In this paper, we propose a bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN), to synthesize medical images from unpaired data. Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain. In addition, cross-entropy and structural similarity index (SSIM) are integrated into the DC-cycleGAN in order to consider both the luminance and structure of samples when synthesizing images. The experimental results indicate that DC-cycleGAN is able to produce promising results as compared with other cycleGAN-based medical image synthesis methods such as cycleGAN, RegGAN, DualGAN, and NiceGAN. The code will be available at https://github.com/JiayuanWang-JW/DC-cycleGAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
