Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2024-11, Vol.14 (1), p.29619-12, Article 29619
Main Authors: Xu, Hao, Olivier, Cecile, Sajidy, Hajar, Pallu, Stéphane, Portier, Hugues, Peyrin, Francoise, Chappard, Christine
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c322t-f52f47f4791d4bc866624a847b4c61da358d74df477e6cea6d598facaba7a9953
container_end_page 12
container_issue 1
container_start_page 29619
container_title Scientific reports
container_volume 14
creator Xu, Hao
Olivier, Cecile
Sajidy, Hajar
Pallu, Stéphane
Portier, Hugues
Peyrin, Francoise
Chappard, Christine
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.
doi_str_mv 10.1038/s41598-024-81333-x
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_cb9f5e0f2ca14afb83de55db31690135</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_cb9f5e0f2ca14afb83de55db31690135</doaj_id><sourcerecordid>3134067482</sourcerecordid><originalsourceid>FETCH-LOGICAL-c322t-f52f47f4791d4bc866624a847b4c61da358d74df477e6cea6d598facaba7a9953</originalsourceid><addsrcrecordid>eNp9kstq3DAUhk1paUKaF-iiCLrpxq118UXLMvQSCGTTrsWxdDzWYEuOJEPmXfqw1YzTNHRRIdDtO790jv6ieEurj7Ti3acoaC27smKi7CjnvHx4UVyyStQl44y9fDa_KK5jPFS51UwKKl8XF1w2lawZvSx-7XCayP0KLtnBakjWOwKJpBGJjwm9Hr0zASZiXcIwgEYyBD-TeHR6DD6FzAcwdotcRohItHcpQExktjr4Uvt5WRMakvzs9wGW8UjsDHuMZI3W7QkQg7iQCSG483pZggc9vileDTBFvH4cr4qfX7_82H0vb---3ew-35Y6p5fKoWaDaHOX1Ihed03TMAGdaHuhG2qA151phclAi41GaEyuXM4EemhByppfFTebrvFwUEvIjwtH5cGq84YPewUhWT2h0r0caqwGpoEKGPqOG6xr03PayIryk9aHTSuncL9iTGq2Uecig0O_RsUpF1XTio5l9P0_6MGvweVMTxSXrGnoiWIblUsZY8Dh6YG0UicrqM0KKltBna2gHnLQu0fptZ_RPIX8-fgM8A2I-cjtMfy9-z-yvwGlhsND</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3133926612</pqid></control><display><type>article</type><title>Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach</title><source>PubMed (Medline)</source><source>Publicly Available Content Database</source><source>Full-Text Journals in Chemistry (Open access)</source><source>Springer Nature - nature.com Journals - Fully Open Access</source><creator>Xu, Hao ; Olivier, Cecile ; Sajidy, Hajar ; Pallu, Stéphane ; Portier, Hugues ; Peyrin, Francoise ; Chappard, Christine</creator><creatorcontrib>Xu, Hao ; Olivier, Cecile ; Sajidy, Hajar ; Pallu, Stéphane ; Portier, Hugues ; Peyrin, Francoise ; Chappard, Christine</creatorcontrib><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.</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. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c322t-f52f47f4791d4bc866624a847b4c61da358d74df477e6cea6d598facaba7a9953</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3133926612/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3133926612?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25732,27903,27904,36991,36992,44569,74873</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39609521$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xu, Hao</creatorcontrib><creatorcontrib>Olivier, Cecile</creatorcontrib><creatorcontrib>Sajidy, Hajar</creatorcontrib><creatorcontrib>Pallu, Stéphane</creatorcontrib><creatorcontrib>Portier, Hugues</creatorcontrib><creatorcontrib>Peyrin, Francoise</creatorcontrib><creatorcontrib>Chappard, Christine</creatorcontrib><title>Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><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.</description><subject>631/114</subject><subject>631/1647</subject><subject>631/57</subject><subject>631/80</subject><subject>639/705</subject><subject>639/766</subject><subject>692/698</subject><subject>692/699</subject><subject>Aged</subject><subject>Anisotropy</subject><subject>Cartilage diseases</subject><subject>Cartilage, Articular - diagnostic imaging</subject><subject>Cartilage, Articular - pathology</subject><subject>Chondrocytes</subject><subject>Chondrocytes and osteocytes</subject><subject>Computed tomography</subject><subject>Deep Learning</subject><subject>Female</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Image processing</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Knee</subject><subject>Male</subject><subject>Middle Aged</subject><subject>multidisciplinary</subject><subject>Osteoarthritis</subject><subject>Osteoarthritis, Knee - diagnostic imaging</subject><subject>Osteoarthritis, Knee - pathology</subject><subject>Osteocytes</subject><subject>Plateaus</subject><subject>Radiation</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Segmentation</subject><subject>Subchondral bone</subject><subject>Synchrotron radiation phase contrast micro-computed tomography</subject><subject>Synchrotrons</subject><subject>Tomography</subject><subject>Watershed</subject><subject>X-Ray Microtomography - methods</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kstq3DAUhk1paUKaF-iiCLrpxq118UXLMvQSCGTTrsWxdDzWYEuOJEPmXfqw1YzTNHRRIdDtO790jv6ieEurj7Ti3acoaC27smKi7CjnvHx4UVyyStQl44y9fDa_KK5jPFS51UwKKl8XF1w2lawZvSx-7XCayP0KLtnBakjWOwKJpBGJjwm9Hr0zASZiXcIwgEYyBD-TeHR6DD6FzAcwdotcRohItHcpQExktjr4Uvt5WRMakvzs9wGW8UjsDHuMZI3W7QkQg7iQCSG483pZggc9vileDTBFvH4cr4qfX7_82H0vb---3ew-35Y6p5fKoWaDaHOX1Ihed03TMAGdaHuhG2qA151phclAi41GaEyuXM4EemhByppfFTebrvFwUEvIjwtH5cGq84YPewUhWT2h0r0caqwGpoEKGPqOG6xr03PayIryk9aHTSuncL9iTGq2Uecig0O_RsUpF1XTio5l9P0_6MGvweVMTxSXrGnoiWIblUsZY8Dh6YG0UicrqM0KKltBna2gHnLQu0fptZ_RPIX8-fgM8A2I-cjtMfy9-z-yvwGlhsND</recordid><startdate>20241128</startdate><enddate>20241128</enddate><creator>Xu, Hao</creator><creator>Olivier, Cecile</creator><creator>Sajidy, Hajar</creator><creator>Pallu, Stéphane</creator><creator>Portier, Hugues</creator><creator>Peyrin, Francoise</creator><creator>Chappard, Christine</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>20241128</creationdate><title>Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach</title><author>Xu, Hao ; Olivier, Cecile ; Sajidy, Hajar ; Pallu, Stéphane ; Portier, Hugues ; Peyrin, Francoise ; Chappard, Christine</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c322t-f52f47f4791d4bc866624a847b4c61da358d74df477e6cea6d598facaba7a9953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>631/114</topic><topic>631/1647</topic><topic>631/57</topic><topic>631/80</topic><topic>639/705</topic><topic>639/766</topic><topic>692/698</topic><topic>692/699</topic><topic>Aged</topic><topic>Anisotropy</topic><topic>Cartilage diseases</topic><topic>Cartilage, Articular - diagnostic imaging</topic><topic>Cartilage, Articular - pathology</topic><topic>Chondrocytes</topic><topic>Chondrocytes and osteocytes</topic><topic>Computed tomography</topic><topic>Deep Learning</topic><topic>Female</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Image processing</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Knee</topic><topic>Male</topic><topic>Middle Aged</topic><topic>multidisciplinary</topic><topic>Osteoarthritis</topic><topic>Osteoarthritis, Knee - diagnostic imaging</topic><topic>Osteoarthritis, Knee - pathology</topic><topic>Osteocytes</topic><topic>Plateaus</topic><topic>Radiation</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Segmentation</topic><topic>Subchondral bone</topic><topic>Synchrotron radiation phase contrast micro-computed tomography</topic><topic>Synchrotrons</topic><topic>Tomography</topic><topic>Watershed</topic><topic>X-Ray Microtomography - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Hao</creatorcontrib><creatorcontrib>Olivier, Cecile</creatorcontrib><creatorcontrib>Sajidy, Hajar</creatorcontrib><creatorcontrib>Pallu, Stéphane</creatorcontrib><creatorcontrib>Portier, Hugues</creatorcontrib><creatorcontrib>Peyrin, Francoise</creatorcontrib><creatorcontrib>Chappard, Christine</creatorcontrib><collection>SpringerOpen</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Hao</au><au>Olivier, Cecile</au><au>Sajidy, Hajar</au><au>Pallu, Stéphane</au><au>Portier, Hugues</au><au>Peyrin, Francoise</au><au>Chappard, Christine</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2024-11-28</date><risdate>2024</risdate><volume>14</volume><issue>1</issue><spage>29619</spage><epage>12</epage><pages>29619-12</pages><artnum>29619</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>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.</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>
fulltext fulltext
identifier ISSN: 2045-2322
ispartof Scientific reports, 2024-11, Vol.14 (1), p.29619-12, Article 29619
issn 2045-2322
2045-2322
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_cb9f5e0f2ca14afb83de55db31690135
source PubMed (Medline); Publicly Available Content Database; Full-Text Journals in Chemistry (Open access); Springer Nature - nature.com Journals - Fully Open Access
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T19%3A36%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Cell%20quantification%20at%20the%20osteochondral%20interface%20from%20synchrotron%20radiation%20phase%20contrast%20micro-computed%20tomography%20images%20using%20a%20deep%20learning%20approach&rft.jtitle=Scientific%20reports&rft.au=Xu,%20Hao&rft.date=2024-11-28&rft.volume=14&rft.issue=1&rft.spage=29619&rft.epage=12&rft.pages=29619-12&rft.artnum=29619&rft.issn=2045-2322&rft.eissn=2045-2322&rft_id=info:doi/10.1038/s41598-024-81333-x&rft_dat=%3Cproquest_doaj_%3E3134067482%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c322t-f52f47f4791d4bc866624a847b4c61da358d74df477e6cea6d598facaba7a9953%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3133926612&rft_id=info:pmid/39609521&rfr_iscdi=true