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
- Author:
Hye Jeon HWANG
1
;
Hyunjong KIM
;
Joon Beom SEO
;
Jong Chul YE
;
Gyutaek OH
;
Sang Min LEE
;
Ryoungwoo JANG
;
Jihye YUN
;
Namkug KIM
;
Hee Jun PARK
;
Ho Yun LEE
;
Soon Ho YOON
;
Kyung Eun SHIN
;
Jae Wook LEE
;
Woocheol KWON
;
Joo Sung SUN
;
Seulgi YOU
;
Myung Hee CHUNG
;
Bo Mi GIL
;
Jae-Kwang LIM
;
Youkyung LEE
;
Su Jin HONG
;
Yo Won CHOI
Author Information
- Publication Type:Original Article
- From:Korean Journal of Radiology 2023;24(8):807-820
- CountryRepublic of Korea
- Language:English
-
Abstract:
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 sty le (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: 0.63vs. 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.
