ArticleJournal of bone and mineral metabolism2022
A morphometric analysis of the osteocyte canaliculus using applied automatic semantic segmentation by machine learning.
Article in Journal of bone and mineral metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- 2D vs. 3D Evaluation of Osteocyte Lacunae - Methodological Approaches, Recommended Parameters, and Challenges: A Narrative Review by the European Calcified Tissue Society (ECTS).Current osteoporosis reports · 2024Review
- Contributions of Resin Cast Etching to Visualising the Osteocyte Lacuno-Canalicular Network Architecture in Bone Biology and Tissue Engineering.Calcified tissue international · 2023Review
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
introductionOsteocytes 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
methodsSix-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.
resultsThe 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.
conclusionWe 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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