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Consistent identification and classification of microglia is essential in understanding potential links with neurological diseases. Currently, there are several ways by which the microglial population and morphology are assessed, including manually or using open\u2010source image analysis platforms, such as\n                    <jats:sc>ImageJ<\/jats:sc>\n                    . A microglial classification module for the\n                    <jats:sc>HALO<\/jats:sc>\n                    digital pathology platform has been developed for this purpose but has not yet been validated within the literature. The current study therefore conducted a comparison of the performance of this\n                    <jats:sc>HALO<\/jats:sc>\n                    module to manual microglial analysis and to automated analysis via\n                    <jats:sc>ImageJ<\/jats:sc>\n                    using both human and rat brain tissue. In 5\u2009\u03bcm thick human tissue, total and activated microglia\/mm\n                    <jats:sup>2<\/jats:sup>\n                    counted by\n                    <jats:sc>HALO<\/jats:sc>\n                    showed strong positive correlations with both manual and\n                    <jats:sc>ImageJ<\/jats:sc>\n                    counts.\n                    <jats:sc>HALO<\/jats:sc>\n                    did not differ from the other methods for total microglia counts; however,\n                    <jats:sc>Halo<\/jats:sc>\n                    did differ from both manual and\n                    <jats:sc>ImageJ<\/jats:sc>\n                    methods in the number of activated microglia detected within the substantia nigra. In 20\u2009\u03bcm rat tissue, total counts derived from\n                    <jats:sc>HALO<\/jats:sc>\n                    showed moderate positive correlations with both manual and\n                    <jats:sc>ImageJ<\/jats:sc>\n                    counting; however, activated counts on\n                    <jats:sc>Halo<\/jats:sc>\n                    were not positively correlated with any method. To our knowledge, this is the first study to systematically compare the\n                    <jats:sc>Halo<\/jats:sc>\n                    module to other common methods of microglia analysis. When applied to 5\u2009\u03bcm tissue, the\n                    <jats:sc>Halo<\/jats:sc>\n                    module is comparable to manual counting and to automated analysis on\n                    <jats:sc>ImageJ<\/jats:sc>\n                    . However, when analyzing thicker tissue,\n                    <jats:sc>Halo<\/jats:sc>\n                    struggles to perform in line with these other methods, particularly for counts of activated microglia, likely due to increased cell density and the morphological complexity of microglia. These results highlight the importance of carefully tailoring image analysis parameters on automated counting methods to suit the needs of the tissue.\n                  <\/jats:p>","DOI":"10.1111\/bpa.70036","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T09:35:07Z","timestamp":1755596107000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of automated and manual approaches for microglial quantification and classification: A focus on the HALO digital pathology platform"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6008-1389","authenticated-orcid":false,"given":"Laura M.","family":"Carr","sequence":"first","affiliation":[{"name":"School of Biomedicine The University of Adelaide  Adelaide South Australia 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Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8677-5151","authenticated-orcid":false,"given":"Sanam","family":"Mustafa","sequence":"additional","affiliation":[{"name":"School of Biomedicine The University of Adelaide  Adelaide South Australia Australia"},{"name":"Australian Research Council Centre of Excellence for Nanoscale Biophotonics The University of Adelaide  Adelaide South Australia Australia"},{"name":"Davies Livestock Research Centre The University of Adelaide  Roseworthy South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6150-8893","authenticated-orcid":false,"given":"Frances","family":"Corrigan","sequence":"additional","affiliation":[{"name":"School of Biomedicine The University of Adelaide  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4380-7600","authenticated-orcid":false,"given":"Lyndsey E.","family":"Collins\u2010Praino","sequence":"additional","affiliation":[{"name":"School of Biomedicine The University of Adelaide  Adelaide South Australia Australia"},{"name":"Australian Research Council Centre of Excellence for Nanoscale Biophotonics The University of Adelaide  Adelaide South Australia 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