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Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach
Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3D) segmentation of chondrocyte and osteocyte lacun...
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description | Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3D) segmentation of chondrocyte and osteocyte lacunae in CC and SCB from human knees, imaged using high resolution (650 nm) synchrotron radiation phase contrast micro-computed tomography (SR phase contrast micro-CT). Our approach is based on marker-controlled watershed (MCW) algorithm combined with a deep learning method (nnU-Net). We demonstrate that incorporating nnU-Net into the MCW process improves the identification and segmentation of cell lacunae. Using this method, we analyzed a subsample of fifteen cores extracted from the central area of the medial tibial plateaus. Several quantitative parameters (lacunar volume fraction, number density, volume, anisotropy and structure model index of cell lacunae) were measured to compare 10 control and 5 osteoarthritic knees. While no significant differences were observed in chondrocytes, osteocytes showed lower anisotropy (width/depth) and a tendency toward more spherical shapes in the osteoarthritic group compared to the control group. The phase contrast underlying the chondro-osseous border allowed to analyze separately CC from SCB in SR phase contrast micro-CT images. This new method may help to better understand the cellular behavior at the osteochondral interface in osteoarthritis. |
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In this study, we propose a new method for the three-dimensional (3D) segmentation of chondrocyte and osteocyte lacunae in CC and SCB from human knees, imaged using high resolution (650 nm) synchrotron radiation phase contrast micro-computed tomography (SR phase contrast micro-CT). Our approach is based on marker-controlled watershed (MCW) algorithm combined with a deep learning method (nnU-Net). We demonstrate that incorporating nnU-Net into the MCW process improves the identification and segmentation of cell lacunae. Using this method, we analyzed a subsample of fifteen cores extracted from the central area of the medial tibial plateaus. Several quantitative parameters (lacunar volume fraction, number density, volume, anisotropy and structure model index of cell lacunae) were measured to compare 10 control and 5 osteoarthritic knees. While no significant differences were observed in chondrocytes, osteocytes showed lower anisotropy (width/depth) and a tendency toward more spherical shapes in the osteoarthritic group compared to the control group. The phase contrast underlying the chondro-osseous border allowed to analyze separately CC from SCB in SR phase contrast micro-CT images. This new method may help to better understand the cellular behavior at the osteochondral interface in osteoarthritis.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-024-81333-x</identifier><identifier>PMID: 39609521</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114 ; 631/1647 ; 631/57 ; 631/80 ; 639/705 ; 639/766 ; 692/698 ; 692/699 ; Aged ; Anisotropy ; Cartilage diseases ; Cartilage, Articular - diagnostic imaging ; Cartilage, Articular - pathology ; Chondrocytes ; Chondrocytes and osteocytes ; Computed tomography ; Deep Learning ; Female ; Humanities and Social Sciences ; Humans ; Image processing ; Imaging, Three-Dimensional - methods ; Knee ; Male ; Middle Aged ; multidisciplinary ; Osteoarthritis ; Osteoarthritis, Knee - diagnostic imaging ; Osteoarthritis, Knee - pathology ; Osteocytes ; Plateaus ; Radiation ; Science ; Science (multidisciplinary) ; Segmentation ; Subchondral bone ; Synchrotron radiation phase contrast micro-computed tomography ; Synchrotrons ; Tomography ; Watershed ; X-Ray Microtomography - methods</subject><ispartof>Scientific reports, 2024-11, Vol.14 (1), p.29619-12, Article 29619</ispartof><rights>The Author(s) 2024</rights><rights>2024. 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In this study, we propose a new method for the three-dimensional (3D) segmentation of chondrocyte and osteocyte lacunae in CC and SCB from human knees, imaged using high resolution (650 nm) synchrotron radiation phase contrast micro-computed tomography (SR phase contrast micro-CT). Our approach is based on marker-controlled watershed (MCW) algorithm combined with a deep learning method (nnU-Net). We demonstrate that incorporating nnU-Net into the MCW process improves the identification and segmentation of cell lacunae. Using this method, we analyzed a subsample of fifteen cores extracted from the central area of the medial tibial plateaus. Several quantitative parameters (lacunar volume fraction, number density, volume, anisotropy and structure model index of cell lacunae) were measured to compare 10 control and 5 osteoarthritic knees. While no significant differences were observed in chondrocytes, osteocytes showed lower anisotropy (width/depth) and a tendency toward more spherical shapes in the osteoarthritic group compared to the control group. The phase contrast underlying the chondro-osseous border allowed to analyze separately CC from SCB in SR phase contrast micro-CT images. This new method may help to better understand the cellular behavior at the osteochondral interface in osteoarthritis.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>39609521</pmid><doi>10.1038/s41598-024-81333-x</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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subjects | 631/114 631/1647 631/57 631/80 639/705 639/766 692/698 692/699 Aged Anisotropy Cartilage diseases Cartilage, Articular - diagnostic imaging Cartilage, Articular - pathology Chondrocytes Chondrocytes and osteocytes Computed tomography Deep Learning Female Humanities and Social Sciences Humans Image processing Imaging, Three-Dimensional - methods Knee Male Middle Aged multidisciplinary Osteoarthritis Osteoarthritis, Knee - diagnostic imaging Osteoarthritis, Knee - pathology Osteocytes Plateaus Radiation Science Science (multidisciplinary) Segmentation Subchondral bone Synchrotron radiation phase contrast micro-computed tomography Synchrotrons Tomography Watershed X-Ray Microtomography - methods |
title | Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach |
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