MedGAN: Medical image translation using GANs
MedGAN: Medical image translation using GANs
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DOI：
10.1016/j.compmedimag.2019.101684
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发表时间：
2020-01-01
影响因子：
5.7
通讯作者：
Yang, Bin
中科院分区：
文献类型：
--
作者：
Armanious, Karim;Jiang, Chenming;Yang, Bin
图像到图像的转换被认为是医学图像分析领域的一个新的前沿，具有许多潜在的应用。然而，最近的方法中有很大一部分提供了基于特定任务架构的个性化解决方案，或者需要通过非端到端培训进行改进。在本文中，我们提出了一个新的框架，名为MedGAN，用于医学图像到图像的翻译，它以端到端的方式在图像级别上运行。MedGAN基于生成对抗网络（GAN）领域的最新进展，将对抗框架与非对抗损失的新组合相结合。我们利用一个神经网络作为一个可训练的特征提取器，惩罚翻译的医学图像和所需的模式之间的差异。此外，风格转移损失被用来匹配所需的目标图像的纹理和精细结构的翻译图像。此外，我们提出了一种新的生成器架构，名为CasNet，它通过编码器-解码器对的渐进式改进来增强翻译后的医学输出的清晰度。在没有任何特定应用修改的情况下，我们将MedGAN应用于三个不同的任务：PET-CT翻译，MR运动伪影校正和PET图像去噪。放射科医生的感知分析和定量评估表明，MedGAN优于其他现有的翻译方法。(C)2019爱思唯尔有限公司版权所有。
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches. (C) 2019 Elsevier Ltd. All rights reserved.