Evidence map›Paper›PMID 42266426›Full record

ArticleBrain communications2026

InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation.

Anna Schroder, James Moggridge, Hamza A Salhab, Caroline Micallef, Jiaming Wu, Melissa Bristow, Fernando Pérez-García, Javier Alvarez-Valle, Sjoerd B Vos, Tarek A Yousry and 5 more

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In one paragraph

Article in Brain communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Anna SchroderDepartment of Computer Science, University College London, London WC1V 6LJ, UK.
James MoggridgeLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3AR, UK.
Hamza A SalhabLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3AR, UK.
Caroline MicallefLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3AR, UK.
Jiaming WuDepartment of Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK.
Melissa BristowHealth Futures, Microsoft Research Cambridge, Cambridge CB1 2FB, UK.
Fernando Pérez-GarcíaHealth Futures, Microsoft Research Cambridge, Cambridge CB1 2FB, UK.
Javier Alvarez-ValleHealth Futures, Microsoft Research Cambridge, Cambridge CB1 2FB, UK.
Sjoerd B VosWestern Australia National Imaging Facility, The University of Western Australia, Perth WA 6009, Australia.
Tarek A YousryLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3AR, UK.
John S ThorntonLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3AR, UK.
Frederik BarkhofDepartment of Translational Neuroscience and Stroke, UCL Institute of Neurology, University College London, London WC1N 3AR, UK.
John S DuncanDepartment of Epilepsy, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.ORCID https://orcid.org/0000-0002-1373-0681
Daniel C AlexanderDepartment of Computer Science, University College London, London WC1V 6LJ, UK.
Matthew Grech-SollarsDepartment of Computer Science, University College London, London WC1V 6LJ, UK.ORCID https://orcid.org/0000-0003-3881-4870

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

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,

Indexed as

Alzheimer’s diseasedeep learningepilepsyhippocampal segmentationmagnetic resonance imaging

Identifiers

PMID42266426
PMCPMC13245398

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.