Electronic cleansing in CT colonography using a generative adversarial network
Electronic cleansing in CT colonography using a generative adversarial network
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使用生成对抗网络进行 CT 结肠成像的电子清洁
DOI：
10.1117/12.2512466
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Hiroyuki Yoshida
中科院分区：
文献类型：
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作者：
Rie Tachibana;Janne J. Nappi;Toru Hironaka;Hiroyuki Yoshida
我们开发了一种基于生成对抗网络（GAN）的新型CT结肠成像（CTC）3D电子清洗（EC）方法。GAN是一种机器学习算法，可以训练它将输入图像直接转换为所需的输出图像，而无需使用显式的手动注释。通过将2D-pix 2 pix GAN模型扩展到基于3D卷积核的体积CTC数据集，开发了3D-GAN EC方案。为了克服通常对成对输入输出训练数据的需要，通过使用自监督学习方案来训练3D-GAN模型，其中训练数据被迭代地构建为来自成对的拟人结肠体模CTC数据集的感兴趣体积（VOI）和来自不可见的临床输入CTC数据集的输入VOI的组合，其中虚拟净化的输出样本对是自监督学习方案。通过使用渐进式清洁方法产生。我们对临床粪便标记CTC病例的初步评估表明，与我们以前的深度学习EC方案相比，3D-GAN EC方案可以大大减少处理时间和EC图像伪影。
We developed a novel 3D electronic cleansing (EC) method for CT colonography (CTC) based on a generative adversarial network (GAN). GANs are machine-learning algorithms that can be trained to translate an input image directly into a desired output image without using explicit manual annotations. A 3D-GAN EC scheme was developed by extending a 2D-pix2pix GAN model to volumetric CTC datasets based on 3D-convolutional kernels. To overcome the usual need for paired input-output training data, the 3D-GAN model was trained by use of a self-supervised learning scheme where the training data were constructed iteratively as a combination of volumes of interest (VOIs) from paired anthropomorphic colon phantom CTC datasets and input VOIs from the unseen clinical input CTC dataset where the virtually cleansed output sample pairs were self-generated by use of a progressive cleansing method. Our preliminary evaluation with a clinical fecal-tagging CTC case showed that the 3D-GAN EC scheme can substantially reduce the processing time and EC image artifacts in comparison to our previous deep-learning EC scheme.