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Bone suppression for chest X-ray image using a convolutional neural filter

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Abstract

Chest X-rays are used for mass screening for the early detection of lung cancer. However, lung nodules are often overlooked because of bones overlapping the lung fields. Bone suppression techniques based on artificial intelligence have been developed to solve this problem. However, bone suppression accuracy needs improvement. In this study, we propose a convolutional neural filter (CNF) for bone suppression based on a convolutional neural network which is frequently used in the medical field and has excellent performance in image processing. CNF outputs a value for the bone component of the target pixel by inputting pixel values in the neighborhood of the target pixel. By processing all positions in the input image, a bone-extracted image is generated. Finally, bone-suppressed image is obtained by subtracting the bone-extracted image from the original chest X-ray image. Bone suppression was most accurate when using CNF with six convolutional layers, yielding bone suppression of 89.2%. In addition, abnormalities, if present, were effectively imaged by suppressing only bone components and maintaining soft-tissue. These results suggest that the chances of missing abnormalities may be reduced by using the proposed method. The proposed method is useful for bone suppression in chest X-ray images.

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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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Authors and Affiliations

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

    Naoki Matsubara, Atsushi Teramoto & Kuniaki Saito

  2. Department of Electrical, Electronic & Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu-city, Gifu, 501-1194, Japan

    Hiroshi Fujita

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  1. Naoki Matsubara
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  2. Atsushi Teramoto
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Correspondence to Atsushi Teramoto.

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Matsubara, N., Teramoto, A., Saito, K. et al. Bone suppression for chest X-ray image using a convolutional neural filter. Phys Eng Sci Med 43, 97–108 (2020). https://doi.org/10.1007/s13246-019-00822-w

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