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Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography

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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 140))

Abstract

Lung cancer is a leading cause of death in human globally. Owing to the low survival rates among lung cancer patients, it is essential to detect and treat cancer at an early stage. In some countries, positron emission tomography (PET)/X-ray computed tomography (CT) examination is also used for the cancer screening in addition to diagnosis and follow-up of treatment. PET/CT images provide both anatomical and functional information of the lung cancer. However, radiologists must examine a large number of these images and therefore, support tools for the localization of lung nodule are desired. This chapter highlights our recent contributions to a hybrid detection scheme of lung nodules in PET/CT images. In the CT image, a massive region is first detected using a cylindrical nodule enhancement filter (CNEF), which is a cylindrical kernel shaped by contrast enhancement filter. Subsequently, high-uptake regions detected by the PET images are merged with the region detected by the CT image. False positives (FPs) among the leading candidates are eliminated by a rule-based classifier and three support vector machines based on the characteristic features obtained from CT and PET images. Experimentally, the detection capability was evaluated using 100 cases of PET/CT images. As a result, the sensitivity in detecting candidates was 83%, with 5 FPs/case. These results indicate that the proposed hybrid method may be useful for the computer-aided detection of lung cancer in clinical practice.

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Acknowledgements

The authors are grateful to Tsuneo Tamaki, Masami Nishio, Osamu Yamamuro, Katsuaki Takahashi, Toshiki Kobayashi of the Nagoya Radiological Diagnosis Foundation. This research was supported in part by a Grant-in-Aid for Scientific Research on Innovative Areas (#26108005), MEXT, Japan; in part by Tateishi Science and Technology Foundation, Japan.

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Authors and Affiliations

  1. Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, Toyoake, Japan

    Atsushi Teramoto

  2. Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan

    Hiroshi Fujita

Authors
  1. Atsushi Teramoto
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  2. Hiroshi Fujita
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Correspondence to Atsushi Teramoto .

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Editors and Affiliations

  1. Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois, USA

    Kenji Suzuki

  2. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Yisong Chen

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Teramoto, A., Fujita, H. (2018). Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography. In: Suzuki, K., Chen, Y. (eds) Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Intelligent Systems Reference Library, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-68843-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-68843-5_4

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  • Online ISBN: 978-3-319-68843-5

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