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

Kaori Tabata, Mana Hashimoto, Haruka Takahashi, Ziyi Wang, Noriyuki Nagaoka, Toru Hara, Hiroshi Kamioka

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalJournal of Bone and Mineral Metabolism
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • FIB-SEM
  • Fluid shear stress
  • Machine learning
  • Osteocyte canaliculus
  • Osteocyte process

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Orthopedics and Sports Medicine
  • Endocrinology

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