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Hippocampal volumes thus provide an early biomarker for these diseases. However, automated segmentation models typically lack robustness to disease-related changes in the hippocampus.<\/jats:p>\n                  <jats:p>In this work, we present the InnerEye hippocampal segmentation tool (InnerEye-HS). This deep learning tool was trained on MRI scans across the Alzheimer\u2019s disease spectrum, providing exposure to varying hippocampal size and topology. We validate the model against manually segmented hippocampi on both clinical dementia and epilepsy datasets collected in clinical settings and compare our model\u2019s performance to four other freely available tools (Automatic Segmentation of Hippocampal Subfields (ASHS), FreeSurfer, FastSurfer and HIPPOSEG).<\/jats:p>\n                  <jats:p>When compared to other freely available tools, the InnerEye-HS model provides the best Dice scores in our hospital dementia dataset (mean = 0.85 \u00b1 0.02, P \u2264 0.0125), and InnerEye-HS and ASHS provided the best Dice scores in our epilepsy dataset (InnerEye-HS mean = 0.85 \u00b1 0.02, ASHS mean = 0.84 \u00b1 0.03). Furthermore, we found a high correlation (R2 = 0.85) between hippocampal volumes extracted from ground-truth segmentations and those extracted from InnerEye-HS segmentations, demonstrating the model\u2019s ability to robustly segment the hippocampus throughout the disease time course.<\/jats:p>\n                  <jats:p>In summary, we present the InnerEye-HS model and demonstrate its advantage over currently available tools. These advantages highlight the clinical utility of our tool.<\/jats:p>","DOI":"10.1093\/braincomms\/fcag183","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T09:55:03Z","timestamp":1779357303000},"source":"Crossref","is-referenced-by-count":0,"title":["InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation"],"prefix":"10.1093","volume":"8","author":[{"given":"Anna","family":"Schroder","sequence":"first","affiliation":[{"name":"Department of Computer Science, University College London , London WC1V 6LJ ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Moggridge","sequence":"additional","affiliation":[{"name":"Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust , London WC1N 3AR ,","place":["UK"]},{"name":"Department of Translational Neuroscience and Stroke, UCL Institute of Neurology, University College London , London WC1N 3AR ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamza A","family":"Salhab","sequence":"additional","affiliation":[{"name":"Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust , London WC1N 3AR ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Micallef","sequence":"additional","affiliation":[{"name":"Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust , London WC1N 3AR ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Medical Physics & Biomedical Engineering, University College London , London WC1E 6BT 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