Year: 2026
Type: article
Abstract: The hippocampus is subject to atrophy in both Alzheimer’s disease and temporal lobe epilepsy. Hippocampal volumes thus provide an early biomarker for these diseases. However, automated segmentation models typically lack robustness to disease-related changes in the hippocampus. In this work, we present the InnerEye hippocampal segmentation tool (InnerEye-HS). This deep learning tool was trained on MRI scans across the Alzheimer’s 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’s performance to four other freely available tools (Automatic Segmentation of Hippocampal Subfields (ASHS), FreeSurfer, FastSurfer and HIPPOSEG). When compared to other freely available tools, the InnerEye-HS model provides the best Dice scores in our hospital dementia dataset (mean = 0.85 ± 0.02, P ≤ 0.0125), and InnerEye-HS and ASHS provided the best Dice scores in our epilepsy dataset (InnerEye-HS mean = 0.85 ± 0.02, ASHS mean = 0.84 ± 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’s ability to robustly segment the hippocampus throughout the disease time course. 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.
Source: Brain Communications
Institutions: University College London, University College London Hospitals NHS Foundation Trust, National Hospital for Neurology and Neurosurgery, UCL Queen Square Institute of Neurology, Microsoft Research (United Kingdom), The University of Western Australia, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,024
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, National Imaging Facility, National Collaborative on Gun Violence Research, National Institute for Health and Care Research, University College London, University of Western Australia, Eisai, University College London Hospitals NHS Foundation Trust, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, Engineering and Physical Sciences Research Council, NIHR Great Ormond Street Hospital Biomedical Research Centre
Awards: G1012222616, G1298807586, G1554142629, G1593117279, G1650819308, G1880935189, Alzheimer's Disease Neuroimaging Initiative, G2100670906, G2152643862, G2189010064, G2340207221, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G3966393351, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G5207404921, G5209990037, G528601746, G5324093878, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5581794527, G5654639750, G5740349702, G5850682332, G6039058924, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G6948409098, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G839208290, G8852128998, G8895907669, G8897063270, G8957659042, G9000039018, G9000122335
OpenAlex ID: W7161952011
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