Kaori Tabata1, Mana Hashimoto2, Haruka Takahashi2, Ziyi Wang2, Noriyuki Nagaoka3, Toru Hara4, Hiroshi Kamioka5. 1. Department of Orthodontics, Okayama University Hospital, Okayama, Japan. 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. 3. Advanced Research Center for Oral and Craniofacial Sciences, Okayama University Dental School, Okayama, Japan. 4. Research Center for Structural Materials, National Institute for Materials Science, Tsukuba, Japan. 5. 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.
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.
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.
Authors: Samuel Remedios; Snehashis Roy; Justin Blaber; Camilo Bermudez; Vishwesh Nath; Mayur B Patel; John A Butman; Bennett A Landman; Dzung L Pham Journal: Proc SPIE Int Soc Opt Eng Date: 2019-03