Chapter title |
Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations
|
---|---|
Chapter number | 20 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46726-9_20 |
Pubmed ID | |
Book ISBNs |
978-3-31-946725-2, 978-3-31-946726-9
|
Authors |
Miaomiao Zhang, William M. Wells III, Polina Golland, William M. Wells, William M. WellsIII |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
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). |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 5 | 31% |
Student > Ph. D. Student | 4 | 25% |
Lecturer | 1 | 6% |
Student > Bachelor | 1 | 6% |
Professor > Associate Professor | 1 | 6% |
Other | 1 | 6% |
Unknown | 3 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 4 | 25% |
Engineering | 3 | 19% |
Computer Science | 3 | 19% |
Agricultural and Biological Sciences | 1 | 6% |
Arts and Humanities | 1 | 6% |
Other | 0 | 0% |
Unknown | 4 | 25% |