The optimal template effect in hippocampus studies of diseased populations
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We evaluate the impact of template choice on template-based segmentation of the hippocampus in epilepsy. Four dataset-specific strategies are quantitatively contrasted: the "closest to average" individual template, the average shape version of the closest to average template, a best appearance template and the best appearance and shape template proposed here and implemented in the open source toolkit Advanced Normalization Tools (ANTS). The cross-correlation similarity metric drives the correspondence model and is used consistently to determine the optimal appearance. Minimum shape distance in the diffeomorphic space determines optimal shape. Our evaluation results show that, with respect to gold-standard manual labeling of hippocampi in epilepsy, optimal shape and appearance template construction outperforms the other strategies for gaining data-derived templates. Our results also show the improvement is most significant on the diseased side and insignificant on the healthy side. Thus, the importance of the template increases when used to study pathology and may be less critical for normal control studies. Furthermore, explicit geometric optimization of the shape component of the unbiased template positively impacts the study of diseased hippocampi.
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The use of high-contrast, age-specific and study-specific template brain images for image registration is a well-validated approach to help overcome confounds from developmental changes.
“The use of high-contrast, age-specific and study-specific template brain images for image registration is a well-validated approach to help overcome some of these confounds (Avants et al., 2010; Fonov et al., 2011)”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Avants et al., 2010
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- Authors
- Avants, B. B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., & Gee, J. C.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Avants, Brian B.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-02
- Volume
- 49
- Issue
- 3
- Pages
- 2457-2466
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2818274
- OpenAlex
- W1967251218
- Cited by
- 640
- References
- 53
- Citations by year
- 2026: 29 · 2025: 44 · 2024: 46 · 2023: 45 · 2022: 58 · 2021: 63 · 2020: 51 · 2019: 49 · 2018: 54 · 2017: 48 · 2016: 31 · 2015: 31 · 2014: 33 · 2013: 21 · 2012: 16
- FWCI
- 13.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (K25 AG027785)
- NIBIB (5R01EB006266-04)
- NIDA (5R01DA022807-02)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- hippocampus segmentation, template selection, epilepsy, diffeomorphic registration, shape distance, hippocampal shape, manual annotation
- MeSH terms
- Algorithms, Atlases as Topic, Epilepsy, Hippocampus, Humans, Image Interpretation, Computer-Assisted
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