Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics
Longitudinal imaging studies involve tracking changes in individuals by repeated image acquisition over time. The goal of these studies is to quantify biological shape variability within and across individuals, and also to distinguish between normal and disease populations. However, data variability is influenced by outside sources such as image acquisition, image calibration, human expert judgment, and limited robustness of segmentation and registration algorithms. In this paper, we propose a two-stage method for the statistical analysis of longitudinal shape. In the first stage, we estimate diffeomorphic shape trajectories for each individual that minimize inconsistencies in segmented shapes across time. This is followed by a longitudinal mixed-effects statistical model in the second stage for testing differences in shape trajectories between groups. We apply our method to a longitudinal database from PREDICT-HD and demonstrate our approach reduces unwanted variability for both shape and derived measures, such as volume. This leads to greater statistical power to distinguish differences in shape trajectory between healthy subjects and subjects with a genetic biomarker for Huntington’s disease (HD).
KeywordsHuntington Disease Spatiotemporal Model Anatomical Shape Shape Trajectory Sasaki Metrics
Unable to display preview. Download preview PDF.
- 1.Davis, B., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. In: Proceedings of IEEE International Conference on Computer Vision (2007)Google Scholar
- 5.Qiu, A., Albert, M., Younes, L., Miller, M.: Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes. NeuroImage 45, S51–S60 (2009)Google Scholar
- 6.Muralidharan, P., Fletcher, P.T.: Sasaki metrics for the analysis of longitudinal data on manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
- 7.Datar, M., Muralidharan, P., Kumar, A., Gouttard, S., Piven, J., Gerig, G., Whitaker, R., Fletcher, P.T.: Mixed-effects shape models for estimating longitudinal changes in anatomy. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds.) STIA 2012. LNCS, vol. 7570, pp. 76–87. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 11.Aylward, E., Mills, J., Liu, D., Nopoulos, P., Ross, C.A., Pierson, R., Paulsen, J.S.: Association between Age and Striatal Volume Stratified by CAG Repeat Length in Prodromal Huntington Disease. PLoS Curr. 3, RRN1235 (2011)Google Scholar
- 12.Kim, E.Y., Johnson, H.J.: Robust multi-site mr data processing: Iterative optimization of bias correction, tissue classification, and registration. Frontiers in Neuroinformatics 7(29) (2013)Google Scholar