Deep learning models to map osteocyte networks from confocal microscopy can successfully distinguish between young and aged bone

• Simon D. Vetter,

  Roles Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

  Affiliations School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom, Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom

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• Charles A. Schurman,

  Roles Methodology, Visualization, Writing – review & editing

  Affiliations Department of Orthopaedic Surgery, University of California, San Francisco, California, United States of America, UC Berkeley/UCSF Graduate Program in Bioengineering, San Francisco, California, United States of America, Buck Institute for Research on Aging, Novato, California, United States of America

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• Tamara Alliston,

  Roles Funding acquisition, Supervision, Writing – review & editing

  Affiliations Department of Orthopaedic Surgery, University of California, San Francisco, California, United States of America, UC Berkeley/UCSF Graduate Program in Bioengineering, San Francisco, California, United States of America, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, United States of America

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• Gregory Slabaugh,

  Roles Conceptualization, Methodology, Supervision, Writing – review & editing

  Affiliations Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom, Alan Turing Institute, British Library, London, United Kingdom

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• Stefaan W. Verbruggen

  Roles Conceptualization, Funding acquisition, Supervision, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: s.verbruggen@qmul.ac.uk

  Affiliations Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom, Centre for Bioengineering and Centre for Predictive in vitro Models, School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom, INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom

  https://orcid.org/0000-0002-2321-1367

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