A morphometric analysis of the osteocyte canaliculus using applied automatic semantic segmentation by machine learning.

Journal: Journal of bone and mineral metabolism
PMID: 35338405

Abstract

INTRODUCTION: Osteocytes play a role as mechanosensory cells by sensing flow-induced mechanical stimuli applied on their cell processes. High-resolution imaging of osteocyte processes and the canalicular wall are necessary for the analysis of this mechanosensing mechanism. Focused ion beam-scanning electron microscopy (FIB-SEM) enabled the visualization of the structure at the nanometer scale with thousands of serial-section SEM images. We applied machine learning for the automatic semantic segmentation of osteocyte processes and canalicular wall and performed a morphometric analysis using three-dimensionally reconstructed images.

Authors

  • Kaori Tabata
    Department of Orthodontics, Okayama University Hospital, Okayama, Japan.
  • Mana Hashimoto
    Department of Orthodontics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata, Kita-ku, Okayama, Okayama, 700-8558, Japan.
  • Haruka Takahashi
    Department of Orthodontics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata, Kita-ku, Okayama, Okayama, 700-8558, Japan.
  • Ziyi Wang
    College of Science, Beijing Forestry University, Beijing, China.
  • Noriyuki Nagaoka
    Advanced Research Center for Oral and Craniofacial Sciences, Okayama University Dental School, Okayama, Japan.
  • Toru Hara
    Research Center for Structural Materials, National Institute for Materials Science, Tsukuba, Japan.
  • Hiroshi Kamioka
    Department of Orthodontics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata, Kita-ku, Okayama, Okayama, 700-8558, Japan. kamioka@md.okayama-u.ac.jp.

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