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

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Journal of Bone and Mineral Metabolism Aims and scope Submit manuscript

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.

Materials and methods

Six-week-old-mice femur were used. Osteocyte processes and canaliculi were observed at a resolution of 2 nm/voxel in a 4 × 4 μm region with 2000 serial-section SEM images. Machine learning was used for automatic semantic segmentation of the osteocyte processes and canaliculi from serial-section SEM images. The results of semantic segmentation were evaluated using the dice similarity coefficient (DSC). The segmented data were reconstructed to create three-dimensional images and a morphological analysis was performed.

Results

The DSC was > 83%. Using the segmented data, a three-dimensional image of approximately 3.5 μm in length was reconstructed. The morphometric analysis revealed that the median osteocyte process diameter was 73.8 ± 18.0 nm, and the median pericellular fluid space around the osteocyte process was 40.0 ± 17.5 nm.

Conclusion

We used machine learning for the semantic segmentation of osteocyte processes and canalicular wall for the first time, and performed a morphological analysis using three-dimensionally reconstructed images.

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Acknowledgements

This study was supported by the NIMS microstructural characterization platform as a program of the Nanotechnology Platform of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (JPMXP09A20NM0039), and the Japan Society for the Promotion of Science in the form of Grants-in-Aid for Research (JP19H03859 and JP19K19295). The authors would like to thank Akiko Nakamura (NIMS), Yuka Hara (NIMS), and Itsuro Kamimura (Maxnet Co., Ltd) for their technical supports.

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Authors and Affiliations

  1. Department of Orthodontics, Okayama University Hospital, Okayama, Japan

    Kaori Tabata

  2. Department of Orthodontics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata, Kita-ku, Okayama, Okayama, 700-8558, Japan

    Mana Hashimoto, Haruka Takahashi, Ziyi Wang & Hiroshi Kamioka

  3. Advanced Research Center for Oral and Craniofacial Sciences, Okayama University Dental School, Okayama, Japan

    Noriyuki Nagaoka

  4. Research Center for Structural Materials, National Institute for Materials Science, Tsukuba, Japan

    Toru Hara

Authors
  1. Kaori Tabata
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  2. Mana Hashimoto
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  3. Haruka Takahashi
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  4. Ziyi Wang
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  5. Noriyuki Nagaoka
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  6. Toru Hara
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  7. Hiroshi Kamioka
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Contributions

HK, KT, MH and ZW designed the study. KT, MH, NN, and TH conducted the study. KT, MH, ZW and HT processed, analyzed, and visualized the data. KT, MH wrote the manuscript and prepared figures. KT, NN, TH, and HK interpreted the data and approved the final version of the manuscript. HK is responsible for the integrity of the data analysis.

Corresponding author

Correspondence to Hiroshi Kamioka.

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This study did not involve human participants.

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This study does not involve human participants and, therefore, does not require informed consent.

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Tabata, K., Hashimoto, M., Takahashi, H. et al. A morphometric analysis of the osteocyte canaliculus using applied automatic semantic segmentation by machine learning. J Bone Miner Metab 40, 571–580 (2022). https://doi.org/10.1007/s00774-022-01321-x

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  • DOI: https://doi.org/10.1007/s00774-022-01321-x

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