{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:15:19Z","timestamp":1784297719522,"version":"3.55.0"},"reference-count":39,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["2023947"],"award-info":[{"award-number":["2023947"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["U19 AG024904"],"award-info":[{"award-number":["U19 AG024904"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time\u2010consuming and error\u2010prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the performance of traditional, deep learning\u2010based and hippocampal subfield segmentation methods within a single investigation. We evaluated 10 automatic hippocampal segmentation methods (FreeSurfer, SynthSeg, FastSurfer, FIRST, e2dhipseg, Hippmapper, Hippodeep, FreeSurfer\u2010Subfields, HippUnfold and HSF) across 3 datasets with manually segmented hippocampus labels. Performance metrics included overlap with manual labels, correlations between manual and automatic volumes, volume similarity, diagnostic group differentiation and systematically located false positives and negatives. Most methods, especially deep learning\u2010based ones that were trained on manual labels, performed well on public datasets but showed more error and variability on clinical data. Many methods tended to over\u2010segment, particularly at the anterior hippocampus border, but were able to distinguish between healthy controls, MCI, and dementia patients based on hippocampal volume. Our findings highlight the challenges in hippocampal segmentation from MRI and the need for more publicly accessible datasets with manual labels across diverse ages and pathological conditions.<\/jats:p>","DOI":"10.1002\/hbm.70200","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T20:31:36Z","timestamp":1743280296000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0275-1476","authenticated-orcid":false,"given":"Sabrina","family":"Sghirripa","sequence":"first","affiliation":[{"name":"Australian Institute for Machine Learning, School of Computer and Mathematical Sciences The University of Adelaide  Adelaide South Australia Australia"},{"name":"Hopwood 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and Medical Research Institute  Adelaide South Australia Australia"},{"name":"Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"For the Alzheimer's Disease Neuroimaging 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