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

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Abstract

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.

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Acknowledgements

The authors gratefully acknowledge partial financial support by the Veteran General Hospitals University System of Taiwan under Grant No. VGHUST110-G7-2-1.

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Authors and Affiliations

  1. Institute of Biomedical Informatics, National Yang-Ming University, 155, Section 2, Linong Street, Taipei, Taiwan

    Heng-sheng Chao

  2. Department of Chest Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, Taiwan

    Heng-sheng Chao, Tsu-Hui Shiao, Chung-Wei Chou & Fang-Chi Lin

  3. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, 155, Section 2, Linong Street, Taipei, Taiwan

    Yu-Te Wu

  4. Institute of Biophotonics, National Yang-Ming University, 155, Section 2, Linong Street, Taipei, Taiwan

    Yu-Te Wu

  5. Brain Research Center, National Yang-Ming University, 155, Section 2, Linong Street, Taipei, Taiwan

    Yu-Te Wu

  6. Graduate Institute of Biomedical Informatics, Taipei Medical University, No. 250 Wu-Xing Street, Taipei, 11031, Taiwan

    Der-Ming Liou

Authors
  1. Heng-sheng Chao
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  2. Tsu-Hui Shiao
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  3. Chung-Wei Chou
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  4. Fang-Chi Lin
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  5. Yu-Te Wu
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Correspondence to Der-Ming Liou.

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Chao, Hs., Shiao, TH., Chou, CW. et al. Computed Tomography Super-Resolution Using a Generative Adversarial Network in Bronchoscopy: A Clinical Feasibility Study. J. Med. Biol. Eng. 41, 592–598 (2021). https://doi.org/10.1007/s40846-021-00614-2

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  • DOI: https://doi.org/10.1007/s40846-021-00614-2

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