Computed Tomography Super-Resolution Using a Generative Adversarial Network in Bronchoscopy: A Clinical Feasibility Study

Heng sheng Chao, Tsu Hui Shiao, Chung Wei Chou, Fang Chi Lin, Yu Te Wu, Der Ming Liou

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Purpose: To evaluate the usefulness of applying computed tomography (CT) images reconstructed by a deep learning super-resolution method to the clinical scenario of planning a real bronchoscopy procedure. Methods: We trained a super-resolution generative adversarial network (SRGAN) to reconstruct CT images to high-resolution (SRGANrc). We tasked three pulmonologists with evaluating the quality of the CT images and the derived virtual bronchoscopies. We also compared the number of bronchi that were segmented by an automatic commercial program with the number of bronchi segmented in different processed thin-sectioned CT images. Results: Regarding the human visual score, the original thin-sectioned CT images received more votes than the reconstructed CT images (SRGANrc) (29 votes versus eight votes). As for the human classification of four high-resolution CT images, the majority of images (83.7%) were classified correctly. Four out of 23 virtual bronchoscopies derived from super-resolution CT images were considered superior. The number of automatically segmented bronchi in super-resolution CT images was on average 1.5 less than that in the original thin-sliced CT images (mean bronchi: 15.1 vs. 16.6). Conclusion: The reconstruction of super-resolution CT images through the SRGAN may have limited applications in the clinical scenarios of our study. In addition to improving the deep-learning algorithm, we need more clinical implementation tests to discover its value.
原文英語
頁(從 - 到)592-598
頁數7
期刊Journal of Medical and Biological Engineering
41
發行號5
DOIs
出版狀態已發佈 - 10月 2021

ASJC Scopus subject areas

  • 生物醫學工程

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