Chapter title |
A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry
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Chapter number | 27 |
Book title |
Medical Imaging and Augmented Reality
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Published in |
Medical imaging and augmented reality : 7th International Conference, MIAR 2016, Bern, Switzerland, August 24-26, 2016 : proceedings. MIAR (Workshop) (7th : 2016 : Bern, Switzerland), August 2016
|
DOI | 10.1007/978-3-319-43775-0_27 |
Pubmed ID | |
Book ISBNs |
978-3-31-943774-3, 978-3-31-943775-0
|
Authors |
Mark Inlow, Shan Cong, Shannon L. Risacher, John West, Maher Rizkalla, Paul Salama, Andrew J. Saykin, Li Shen, for the ADNI, for the ADNI |
Abstract |
In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer's disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 2 | 20% |
Professor | 1 | 10% |
Student > Doctoral Student | 1 | 10% |
Other | 1 | 10% |
Researcher | 1 | 10% |
Other | 1 | 10% |
Unknown | 3 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 1 | 10% |
Computer Science | 1 | 10% |
Psychology | 1 | 10% |
Neuroscience | 1 | 10% |
Medicine and Dentistry | 1 | 10% |
Other | 0 | 0% |
Unknown | 5 | 50% |