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Proceedings Paper

Freesurfer-initialized large deformation diffeomorphic metric mapping with application to Parkinson's disease
Author(s): Jingyun Chen; Samantha J. Palmer; Ali R. Khan; Martin J. Mckeown; Mirza Faial Beg
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Paper Abstract

We apply a recently developed automated brain segmentation method, FS+LDDMM, to brain MRI scans from Parkinson's Disease (PD) subjects, and normal age-matched controls and compare the results to manual segmentation done by trained neuroscientists. The data set consisted of 14 PD subjects and 12 age-matched control subjects without neurologic disease and comparison was done on six subcortical brain structures (left and right caudate, putamen and thalamus). Comparison between automatic and manual segmentation was based on Dice Similarity Coefficient (Overlap Percentage), L1 Error, Symmetrized Hausdorff Distance and Symmetrized Mean Surface Distance. Results suggest that FS+LDDMM is well-suited for subcortical structure segmentation and further shape analysis in Parkinson's Disease. The asymmetry of the Dice Similarity Coefficient over shape change is also discussed based on the observation and measurement of FS+LDDMM segmentation results.

Paper Details

Date Published: 27 March 2009
PDF: 9 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725931 (27 March 2009); doi: 10.1117/12.810854
Show Author Affiliations
Jingyun Chen, Simon Fraser Univ. (Canada)
Samantha J. Palmer, Brain Research Ctr., Univ. of British Columbia (Canada)
Ali R. Khan, Simon Fraser Univ. (Canada)
Martin J. Mckeown, Brain Research Ctr., Univ. of British Columbia (Canada)
Mirza Faial Beg, Simon Fraser Univ. (Canada)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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