The Optimal Template Effect in Hippocampus Studies of Diseased Populations

By Brian B. Avants, Paul Yushkevich, John Pluta, David Minkoff, Marc Korczykowski, John A. Detre, James C. Gee. NeuroImage, 2009

644 citations recorded by the source corpus.

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.

Subfields: Biophysics, Cellular and Molecular Neuroscience, Neurology

View Paper: https://www.semanticscholar.org/paper/2b473368ee54d40e486dadeef17ba40a62fcacea

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Citations in the corpus

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements (2014, methodology): “An optimal template (Avants et al., 2010b), i.e., a mean shape and intensity image representation of a particular cohort, is first constructed using structural MRI data.”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, positive): “Avants et al. (2010) proposed an average template generation by finding minimum shape distance in the diffeomorphic space.”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “, 2010); (2) brain extraction using an optimal population-specific template created by a Symmetric Group Normalization framework (Avants et al., 2010); (3) Atropos probabilistic six-tissue segmentation (Avants, Tustison, Wu, Cook, & Gee, 2011b); (4) DiReCT-based cortical thickness estimation (Das, A”
Investigating White Matter Development in Infancy and Early Childhood using Myelin Water Faction and Relaxation Time Mapping (2012, methodology): “This was performed using the buildtemplateparallel.sh script distributed with the ANTs package (Avants et al., 2010).”
A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease (2018, methodology): “This was based on the Hammers et al., (2003) All template creation and registration procedures were performed using the ANTs package (Avants et al., 2010, 2011).”
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data (2011, methodology): “We then exploit the ground-truth labeling of the training data to build both a group template [49] and also prior probability maps for each of the three major tissues in the cerebrum.”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, background): “Several studies have conducted empirical comparisons of these different atlas selection strategies in various MAS applications (Aljabar et al., 2007; Acosta et al., 2011; Avants et al., 2010; Lötjönen et al., 2010; Ramus and Malandain, 2010), concluding that sum of squared differences and cross-corr”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “…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), and is increasingly adopted in both structural and functional studies of (especially early) development…”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, methodology): “The SyGN (symmetric groupwise normalization, pronounced " sign " ) method implements this approach and is fully explained in (Avants et al., 2010b).”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “1) Symmetric multivariate template construction [122] using the data described in [123].”