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

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 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.

Keywords: Artificial intelligence; Computed tomography; Interstitial lung disease; Quantification.

Fig. 1. Flowchart of patient inclusion for the study. A total of 98920 CT slices were initially collected from 818 patients, of which 93911 CT slices from 668 patients were randomly selected and used to develop the CT conversion algorithm in a previous study. The remaining 5009 CT slices from 150 individual patients were included in this study. A thoracic radiologist, who was blinded to any measurements or results in the study, selected 350 CT slices (50 per group) from 150 patients for the test set, based on the visual review criteria. ILD = interstitial lung disease, CT = computed tomography, GAN = generative adversarial network

Fig. 2. Schematic flow diagram of the conversion of original computed tomography (CT) images to the target CT style (Group 1 CT style) using a routable generative adversarial network (RouteGAN). All 300 CT slices, 50 CT slices for each of the six groups, were converted to Group 1 CT style using the RouteGAN to assess the effect of CT style conversion on the quantification of CT patterns of interstitial lung disease (ILD). Quantification of regional CT patterns of ILD was performed on both original and converted CT images using deep learning-based ILD quantification. For the reference standard quantification of ILD, a thoracic radiologist who was blinded to the quantification results of the software manually drew the six CT patterns of ILD on the original CT slices. The quantifications on the original or converted CT images were compared with the radiologist’s manual quantifications, which were used as the reference standard. CNN = convolutional neural network

Fig. 3. Confusion matrixes of pixel-wise analysis of interstitial lung disease (ILD) quantification on the original and converted computed tomography (CT) images in group 2 to 7 in comparison with the radiologist’s quantifications. The predicted labels of the original and converted CT quantifications of ILD are shown along the x-axis, and true labels (i.e., the reference standard quantification by a thoracic radiologist) are shown along the y-axis. Confusion matrixes show the ratio of |pixel number of S ∩ R||pixel number of R|, where S is the area quantified by a software as one of the six patterns in the original or converted CT images, and R is the area quantified as one of six patterns in the reference standard. The numbers in parentheses are pixel numbers of S ∩ R. Many pixels that were incorrectly classified as ground-glass opacity (GGO) or reticulation on the original CT images were correctly classified as honeycombing or reticulation on the converted CT images. For GGO patterns, some pixels incorrectly classified as reticulation after CT conversion were correctly classified as GGO on the original CT images.

Fig. 4. Example of the conversion of group 3 computed tomography (CT) slices of a man with interstitial lung disease (ILD) into the target CT style using a routable generative adversarial network, and the quantification of ILD on the original CT, converted CT, and radiologist’s reference standard. The dice similarity coefficient (DSC) values of the quantified total abnormalities, fibrosis score, honeycombing, reticulation, and ground-glass opacity (GGO) on the original CT images were 0.86, 0.44, 0.50, 018, and 0.04, respectively, with the radiologist’s quantification used as the reference standard. After CT image conversion, the DSC values were 0.88, 0.88, 0.78, 0.71, and 0.18 for total abnormalities, fibrosis score, honeycombing, reticulation, and GGO, respectively, on the converted CT images, which were higher than on the original CT images. The five radiologists’ mean visual accuracy scores of the segmentations on the original, converted, and reference standard images were 5.40 ± 1.35, 6.80 ± 1.17, and 8.80 ± 1.17, respectively. CNN = convolutional neural network

Fig. 5. Example of the conversion of a group 4 computed tomography (CT) slice of a male patient with interstitial lung disease (ILD) into the target CT style using a routable generative adversarial network, and the quantification of ILD on each CT image. On the original CT images, the dice similarity coefficient (DSC) values of total abnormalities, fibrosis score, honeycombing, and reticulation quantifications were 0.78, 0.59, 0.00, and 0.60, respectively. After CT image conversion, the DSC values on the converted CT images were 0.86, 0.85, 0.14, and 0.76 for total abnormalities, fibrosis score, honeycombing, and reticulation, respectively. The five radiologists’ mean visual accuracy scores of the segmentations on the original, converted, and reference standard images were 7.20 ± 1.60, 8.60 ± 0.80, and 9.00 ± 0.89, respectively. CNN = convolutional neural network