Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease.

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software.
Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system.
Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher ( P < 0.001) and less variable on converted CT.
Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.
Competing Interests: Joon Beom Seo, Namkug Kim, and Ho Yun Lee, contributing editors of the Korean Journal of Radiology, were not involved in the editorial evaluation or decision to publish this article. Jong Chul Ye, Hyunjong Kim, Joon Beom Seo, Sang Min Lee, and Hye Jeon Hwang hold a patent for tomography image processing method using single neural network based on unsupervised learning for image standardization and apparatus therefor (Patent No. KR-10-2021-0040878). In this study, this patented item was used. Joon Beom Seo and Namkug Kim hold a patent on a method for an automatic classifier of lung diseases (Patent No. KR-10-0998630) and have received royalty payments from Coreline Soft, Co., Ltd. Joon Beom Seo, Namkug Kim, Sang Min Lee holds stock/stock options in Coreline Soft, Co., Ltd., Korea. Hee Jun Park was an employee of Coreline Soft, Co., Ltd., Korea. All remaining authors have declared no conflicts of interest.
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