| Automatic segmentation of rodent spinal cord diffusion MR images. | |
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MedLine Citation:
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PMID: 20564582 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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MRI, is a key tool for noninvasive spinal cord lesion analysis; however, accurate, quantitative methods for this analysis are lacking. A new, multistep, multidimensional approach, utilizing the classification expectation maximization algorithm, is proposed for MRI segmentation of spinal cord tissues. Diffusion tensor imaging is used to generate multiple images of each spinal slice, with different diffusion direction weightings. The maximum likelihood tissue classifications are then jointly estimated to produce a binary classification image, corresponding to voxels containing either spinal cord or background. Edge detection is employed to find a nonparametric curve encapsulating the entire spinal cord. The algorithm is evaluated using data from in vivo diffusion tensor imaging of control and injured mouse spinal cords. The algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation in the presence of significant injury. The results of the method are shown to be at least on par with expert manual segmentation. |
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Authors:
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Vanessa K Tidwell; Joong H Kim; Sheng-Kwei Song; Arye Nehorai |
Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine Volume: 64 ISSN: 1522-2594 ISO Abbreviation: Magn Reson Med Publication Date: 2010 Sep |
Date Detail:
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Created Date: 2010-08-31 Completed Date: 2011-01-25 Revised Date: 2011-05-10 |
Medline Journal Info:
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Nlm Unique ID: 8505245 Medline TA: Magn Reson Med Country: United States |
Other Details:
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Languages: eng Pagination: 893-901 Citation Subset: IM |
Copyright Information:
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2010 Wiley-Liss, Inc. |
Affiliation:
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Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA. vkt2@ese.wustl.edu |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Animals Artificial Intelligence* Diffusion Magnetic Resonance Imaging / methods* Female Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Mice Mice, Inbred C57BL Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity Spinal Cord / anatomy & histology* |
| Grant Support | |
ID/Acronym/Agency:
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NS047592/NS/NINDS NIH HHS; R01 NS047592-04/NS/NINDS NIH HHS; R01 NS047592-07/NS/NINDS NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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