Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations.

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2016
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Abstract
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).
Reference Key
zhang2016lowdimensionalmedical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
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
URL
Keywords
Diabetic retinopathy artificial intelligence Machine learning Optical coherence tomography ophthalmology retina neovascular age-related macular degeneration

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