Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations.
Clicks: 303
ID: 60979
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).
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zhang2016lowdimensionalmedical
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| Authors | Zhang, Miaomiao;Wells, William M;Golland, Polina; |
| Journal | medical image computing and computer-assisted intervention : miccai international conference on medical image computing and computer-assisted intervention |
| Year | 2016 |
| DOI | 10.1007/978-3-319-46726-9_20 |
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