Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis
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 a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/61967 |
| _version_ | 1826188340023525376 |
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| author | Qiu, Anqi Brown, Timothy Fischl, Bruce Ma, Jun Miller, Michael I. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Qiu, Anqi Brown, Timothy Fischl, Bruce Ma, Jun Miller, Michael I. |
| author_sort | Qiu, Anqi |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T07:58:10Z |
| format | Article |
| id | mit-1721.1/61967 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:58:10Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/619672022-09-30T01:22:52Z Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis Qiu, Anqi Brown, Timothy Fischl, Bruce Ma, Jun Miller, Michael I. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Fischl, Bruce Fischl, Bruce 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. National Institutes of Health (U.S) (U54 EB005149) National Institute for Neurological Disorders and Stroke (R01 NS052585-01) National Institute for Biomedical Imaging and Bioengineering (R01 EB001550) National Institute for Biomedical Imaging and Bioengineering (R01EB006758) National Center for Research Resources (U.S.) (P41 RR15241, P41-RR14075, R01 RR16594-01A1 , and the NCRR BIRN Morphometric Project BIRN002, U24 RR021382) Singapore. Agency for Science, Technology and Research (Grant R-397-000- 058-133) Singapore. Agency for Science, Technology and Research (SERC 082 101 0025) Singapore. Agency for Science, Technology and Research (SICS 09/1/1/001) 2011-03-25T15:45:09Z 2011-03-25T15:45:09Z 2010-05 2009-01 Article http://purl.org/eprint/type/JournalArticle 1057-7149 INSPEC Accession Number: 11304719 http://hdl.handle.net/1721.1/61967 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 20129863 en_US http://dx.doi.org/10.1109/tip.2010.2042099 IEEE Transactions on Image Processing 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. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Qiu, Anqi Brown, Timothy Fischl, Bruce Ma, Jun Miller, Michael I. Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis |
| title | Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis |
| title_full | Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis |
| title_fullStr | Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis |
| title_full_unstemmed | Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis |
| title_short | Atlas Generation for Subcortical and Ventricular Structures With Its Applications in Shape Analysis |
| title_sort | atlas generation for subcortical and ventricular structures with its applications in shape analysis |
| url | http://hdl.handle.net/1721.1/61967 |
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