A comparison of accurate automatic hippocampal segmentation methods
Open accessAbstract
The hippocampus is one of the first brain structures affected by Alzheimer's disease (AD). While many automatic methods for hippocampal segmentation exist, few studies have compared them on the same data. In this study, we compare four fully automated hippocampal segmentation methods in terms of their conformity with manual segmentation and their ability to be used as an AD biomarker in clinical settings. We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods. The effect size and classification performance is measured for AD versus normal control (NC) groups and for stable mild cognitive impairment (sMCI) versus progressive mild cognitive impairment (pMCI) groups. Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ=0.894). The largest effect size between AD versus NC and sMCI versus pMCI is produced by FreeSurfer with error correction. We further show that, using only hippocampal volume, age, and sex as features, the area under the receiver operating characteristic curve reaches up to 0.8813 for AD versus NC and 0.6451 for sMCI versus pMCI. However, the automatic segmentation methods are not significantly different in their performance.
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High number of Freesurfer exclusions based on detailed visual verification of individual ROIs was previously reported.
“High number of Freesurfer exclusions based on detailed visual verification of individual ROIs was previously reported (Mccarthy 2015; Zandifar et al., 2017).”
From A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies (2020) · cites this paper as Zandifar et al., 2017
Experiment 2: results (p. 5) says FreeSurfer had the largest number of segmentation failures, and Limitations (p. 9) reports 101 subjects for whom the FreeSurfer pipeline could not estimate hippocampal volume. The paper says subjects were included only if they passed quality control, but it does not say the FreeSurfer exclusions resulted from detailed visual verification of individual ROIs. (LLM, conf=0.88)
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Self-citationLaroy, M., Weydts, J., Vansteelandt, K., Emsell, L., Adamson, C. L., Sienaert, P. A., Dols, A., Rhebergen, D., Stek, M. L., Vandenbulcke, M., et al. (2019) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2019.07.006 · Cited by 2 · see citing works
Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation
Cao, P., Sheng, Q., Fang, S., Li, X., Ning, G., & Pan, Q. (2020) · IEEE Access · doi:10.1109/access.2020.2984661 · Cited by 2 · see citing works
Automatization and validation of the hippocampal-to-ventricle ratio in a clinical samplePreprint
Fernandez‐Lozano, S., Fonov, V., Schoemaker, D., Pruessner, J. C., Potvin, O., Duchesne, S., Collins, D. L., & Initiative, A. D. N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.12.588928 · Cited by 2 · see citing works
Self-citationVasanth, K., Thilakan, D., Ashwini, N., & Anagha, J. A. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.44241 · Cited by 1 · see citing works
A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation
Vambe, T. C., Viriri, S., & Gwetu, M. V. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-93314-2_2
Carter, L., Surti, T., Abildskov, T. J., Shum, E., Hertzler, A., Dodda, M., Rodriguez, A., Brown, C., Sanchez, J. R., Tar, D., et al. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100361
- Authors
- Zandifar, A., Fonov, V., Coupé, P., Pruessner, J., & Collins, D. L.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- McGill University Research Centre for Studies in Aging (CA)
- Corresponding author
- Collins, D. Louis
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 155
- Pages
- 383-393
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2604915122
- Cited by
- 41
- References
- 55
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 4 · 2020: 5 · 2019: 10 · 2018: 9 · 2017: 1
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 361 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- NeuroImage (acceptedVersion)
- PubMed (publishedVersion)
- KOPS (University of Konstanz) (submittedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- Funders
- Canadian Institutes of Health Research (MOP-111169)
- Fonds de Research Santé Québec Pfizer Innovation fund (25262)
- NSERC CREATE (4140438 - 2012)
- Famille Louise & André Charron
- Alzheimer's Disease Neuroimaging Initiative (ADNI)
- National Institutes of Health (U01 AG024904)
- DOD ADNI
- Department of Defense (W81XWH-12-2-0012)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai, Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd and its affiliated company
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development, LLC.
- Lumosity
- Lundbeck
- Merck & Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer, Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- ADNI clinical sites in Canada
- Canada Foundation for Innovation (CFI)
- ministére de l'Économie, de la Science et de l'Innovation du Québec (MESI)
- Fonds de recherche du Québec - Nature et technologies (FRQ-NT)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- hippocampus segmentation, Alzheimer's disease, mild cognitive impairment, automatic segmentation, FreeSurfer, error correction, hippocampal volume, Alzheimer's disease biomarkers, method comparison
- MeSH terms
- Aged, Alzheimer Disease, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Cognitive Dysfunction
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