Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations
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nihms850833.pdf
Description
Accepted version
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540.11 KB
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Author(s)
Zhang, Miaomiao
Wells, William M.
Golland, Polina
Date Issued
2016
Publisher
Springer Nature America, Inc
Citation
Zhang, Miaomiao, Wells, William M. and Golland, Polina. 2016. "Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations."
Version
Author's final manuscript
Abstract
© Springer International Publishing AG 2016. Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious “curse of dimensionality” coupled with a small sample size. In this paper,we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors,we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-46726-9_20
