Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis
Name
Qiu-2010-Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis.pdf
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945.12 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Qiu, Anqi
Brown, Timothy
Fischl, Bruce
Ma, Jun
Miller, Michael I.
Date Issued
May 2010
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Citation
Anqi Qiu et al. “Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis.” Image Processing, IEEE Transactions on 19.6 (2010): 1539-1547. © Copyright 2010 IEEE
Version
Final published version
Abstract
Atlas-driven morphometric analysis has received great attention for studying anatomical shape variation across clinical populations in neuroimaging research as it provides a local coordinate representation for understanding the family of anatomic observations. We present a procedure for generating atlas of subcortical and ventricular structures, including amygdala, hippocampus, caudate, putamen, globus pallidus, thalamus, and lateral ventricles, using the large deformation diffeomorphic metric atlas generation algorithm. The atlas was built based on manually labeled volumes of 41 subjects randomly selected from the database of Open Access Series of Imaging Studies (OASIS, 10 young adults, 10 middle-age adults, 10 healthy elders, and 11 patients with dementia). We show that the estimated atlas is representative of the population in terms of its metric distance to each individual subject in the population. In the application of detecting shape variations, using the estimated atlas may potentially increase statistical power in identifying group shape difference when comparing with using a single subject atlas. In shape-based classification, the metric distances between subjects and each of within-class estimated atlases construct a shape feature space, which allows for performing a variety of classification algorithms to distinguish anatomies.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/tip.2010.2042099
