27 March 2009 Freesurfer-initialized large deformation diffeomorphic metric mapping with application to Parkinson's disease
Author Affiliations +
Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725931 (2009); doi: 10.1117/12.810854
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jingyun Chen, Samantha J. Palmer, Ali R. Khan, Martin J. Mckeown, Mirza Faial Beg, "Freesurfer-initialized large deformation diffeomorphic metric mapping with application to Parkinson's disease", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725931 (27 March 2009); doi: 10.1117/12.810854; http://dx.doi.org/10.1117/12.810854
PROCEEDINGS
9 PAGES


SHARE
KEYWORDS
Image segmentation

Brain

Neuroimaging

Parkinson's disease

Control systems

Magnetic resonance imaging

Thalamus

Back to Top