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Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks

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Abstract

Purpose

Early detection and treatment of lung cancer holds great importance. However, pulmonary-nodule classification using CT images alone is difficult to realize. To address this concern, a method for pulmonary-nodule classification based on a deep convolutional neural network (DCNN) and generative adversarial networks (GAN) has previously been proposed by the authors. In that method, the said classification was performed exclusively using axial cross sections of pulmonary nodules. During actual medical-examination procedures, however, a comprehensive judgment can only be made via observation of various pulmonary-nodule cross sections. In the present study, a comprehensive analysis was performed by extending the application of the previously proposed DCNN- and GAN-based automatic classification method to multiple cross sections of pulmonary nodules.

Methods

Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. Firstly, multiplanar images of the pulmonary nodule are generated. Classification training was performed for three DCNNs. A certain pretraining was initially performed using GAN-generated nodule images. This was followed by fine-tuning of each pretrained DCNN using original nodule images provided as input.

Results

As a result of the evaluation, the specificity was 77.8% and the sensitivity was 93.9%. Additionally, the specificity was observed to have improved by 11.1% without any reduction in the sensitivity, compared to our previous report.

Conclusion

This study reports development of a comprehensive analysis method to classify pulmonary nodules at multiple sections using GAN and DCNN. The effectiveness of the proposed discrimination method based on use of multiplanar images has been demonstrated to be improved compared to that realized in a previous study reported by the authors. In addition, the possibility of enhancing classification accuracy via application of GAN-generated images, instead of data augmentation, for pretraining even for medical datasets that contain relatively few images has also been demonstrated.

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References

  1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30

    Article  Google Scholar 

  2. National Lung Screening Trial Research Team (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365(5):395–409

    Article  Google Scholar 

  3. Asano F, Aoe M, Ohsaki Y, Okada Y, Sasada S, Sato S, Suzuki E, Senba H, Fujino S, Ohmori K (2012) Deaths and complications associated with respiratory endoscopy: a survey by the Japan Society for Respiratory Endoscopy in 2010. Respirology 17(3):478–485

    Article  Google Scholar 

  4. Teramoto A, Fujita H, Yamamuro O, Tamaki T (2016) Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys 43(6):2821–2827

    Article  Google Scholar 

  5. Dey R, Lu Z, Hong Y (2018) Diagnostic classification of lung nodules using 3D neural networks. In: IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 774–778

  6. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672–2680

    Google Scholar 

  7. Onishi Y, Teramoto A, Tsujimoto M, Tsukamoto T, Saito K, Toyama H, Imaizumi K, Fujita H (2019) Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. BioMed research international, vol 2019, Article ID 6051939, pp 1–9

    Google Scholar 

  8. Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Çukur T (2019) Image synthesis in multi-contrast MRI with conditional generative adversarial networks. In: IEEE transactions on medical imaging

  9. Jiang C, Zhang Q, Ge Y, Liang D, Yang Y, Liu X, Zheng H, Hu Z (2019) Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging. In: Medical imaging 2019: physics of medical imaging, vol 10948, p 1094836

  10. Qin Y, Zheng H, Huang X, Yang J, Zhu YM (2018) Pulmonary nodule segmentation with CT sample synthesis using adversarial networks. Med Phys 46(3):1218–1229

    Article  Google Scholar 

  11. Ge Y, Xue Z, Cao T, Liao S (2019) Unpaired whole-body MR to CT synthesis with correlation coefficient constrained adversarial learning. In: Medical imaging 2019: image processing, vol 10949, p 1094905

  12. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra MK, Zhang Y, Sun L, Wang G (2018) Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans Med Imag 37(6):1348–1357

    Article  Google Scholar 

  13. Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H (2017) Automated classification of lung cancer types from cytological images using deep convolutional neural networks. BioMed Research International, vol. 2017, Article ID 4067832, 6 pages

  14. Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 12(10):1799–1808

    Article  Google Scholar 

  15. Ciompi F, Chung K, van Riel SJ, Adiyoso Seito AA, Gerke PK, Jacobs C, Scholten ET, Schaefer-Prokop C, Wille MM, Marchianò A, Pastorino U, Prokop M, van Ginneken B (2017) Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep 7(46479):1–11

    Google Scholar 

  16. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. arXiv preprint arXiv:1701.07875

  17. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  18. Alexios K, Keith M, Xiaoli L, Jun H (2017) Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J Cheminform 9:42

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy, No. 26108005) and a Grant-in-Aid for Scientific Research (No. 17K09070), MEXT, Japan.

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Authors and Affiliations

  1. Graduate School of Health Sciences, Fujita Health University, 1–98 Dengakugakubo, Kutsukake cho, Toyoake City, Aichi, 470-1192, Japan

    Yuya Onishi, Atsushi Teramoto & Kuniaki Saito

  2. Fujita Health University Hospital, 1–98 Dengakugakubo, Kutsukake cho, Toyoake City, Aichi, 470-1192, Japan

    Masakazu Tsujimoto

  3. School of Medicine, Fujita Health University, 1–98 Dengakugakubo, Kutsukake cho, Toyoake City, Aichi, 470-1192, Japan

    Tetsuya Tsukamoto, Hiroshi Toyama & Kazuyoshi Imaizumi

  4. Gifu University, 1–1 Yanagido, Gifu, 501-1194, Japan

    Hiroshi Fujita

Authors
  1. Yuya Onishi
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  2. Atsushi Teramoto
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  3. Masakazu Tsujimoto
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  4. Tetsuya Tsukamoto
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  5. Kuniaki Saito
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  6. Hiroshi Toyama
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  7. Kazuyoshi Imaizumi
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  8. Hiroshi Fujita
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Corresponding author

Correspondence to Atsushi Teramoto.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Onishi, Y., Teramoto, A., Tsujimoto, M. et al. Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks. Int J CARS 15, 173–178 (2020). https://doi.org/10.1007/s11548-019-02092-z

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  • DOI: https://doi.org/10.1007/s11548-019-02092-z

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