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To address this challenge, we assembled an automated image analysis pipeline, FiNuTyper (<jats:styled-content>Fi<\/jats:styled-content>ber and <jats:styled-content>Nu<\/jats:styled-content>cleus <jats:styled-content>Typer<\/jats:styled-content>).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We integrated recently developed deep learning\u2010based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle, and utilized SERCA1 and SERCA2 as type\u2010specific myonucleus and myofiber markers after validating them against the traditional use of MyHC isoforms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Parameters including cross\u2010sectional area, myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber\u2010type\u2010specific manner, revealing that a large degree of sex\u2010 and muscle\u2010related heterogeneity could be detected using the pipeline. Our platform was also tested on pathological muscle tissue (ALS and IBM) and adapted for the detection of other resident cell types (leucocytes, satellite cells, capillary endothelium).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition and structure of healthy and diseased human skeletal muscle.<\/jats:p><\/jats:sec>","DOI":"10.1111\/apha.13982","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T12:46:01Z","timestamp":1682426761000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["FiNuTyper: Design and validation of an automated deep learning\u2010based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle"],"prefix":"10.1111","volume":"239","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6524-9657","authenticated-orcid":false,"given":"August","family":"Lundquist","sequence":"first","affiliation":[{"name":"Department of Cell and Molecular Biology Karolinska Institutet  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8664-7531","authenticated-orcid":false,"given":"Enik\u0151","family":"L\u00e1z\u00e1r","sequence":"additional","affiliation":[{"name":"Department of Cell and Molecular Biology Karolinska Institutet  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan S.","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Cell and Molecular Biology Karolinska Institutet  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2746-8581","authenticated-orcid":false,"given":"Eric 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