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

International Journal of Computer Assisted Radiology and Surgery volume 15, pages 173–178 (2020)Cite this article

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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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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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

Keywords

  • Pulmonary nodule
  • CT imaging
  • Multiplanar analysis
  • Classification
  • DCNN
  • GAN