%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 9
%N 3
%P e23328
%T Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test
%A Park,Ho Young
%A Bae,Hyun-Jin
%A Hong,Gil-Sun
%A Kim,Minjee
%A Yun,JiHye
%A Park,Sungwon
%A Chung,Won Jung
%A Kim,NamKug
%+ Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine & Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, 82 2 3010 1548, hgs2013@gmail.com
%K generative adversarial network
%K unsupervised deep learning
%K computed tomography
%K synthetic body images
%K visual Turing test
%D 2021
%7 17.3.2021
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: Generative adversarial network (GAN)–based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. Objective: The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. Methods: We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with <5 years of experience [Group I], 4 radiologists with 5-10 years of experience [Group II], and 2 radiologists with >10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image. Results: The mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4% vs 1500/3000, 50.0%, respectively; P<.001). However, in terms of identifying synthetic images as fake, there was no significant difference in the specificity between the visual Turing test and random guessing (779/1500, 51.9% vs 750/1500, 50.0%, respectively; P=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0%; Group II, 726/1200, 60.5%; and Group III, 359/600, 59.8%; P=.36). Interreader agreements were poor (κ=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details. Conclusions: The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details. 
%M 33609339
%R 10.2196/23328
%U https://medinform.jmir.org/2021/3/e23328
%U https://doi.org/10.2196/23328
%U http://www.ncbi.nlm.nih.gov/pubmed/33609339