Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics

Prasanna Muralidharan (1) , James Fishbaugh (1) , Hans J. Johnson (2) , Stanley Durrleman (3) , Jane S. Paulsen (2) , Guido Gerig (1) , Thomas P. Fletcher (4)

1 SCI Institute - Scientific Computing and Imaging Institute
2 Department of Psychiatry
3 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
4 School of computing [UTAH]

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 longitu-dinal 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 ap-proach 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).

Domaines: Imagerie médicale

Prasanna Muralidharan, James Fishbaugh, Hans J. Johnson, Stanley Durrleman, Jane S. Paulsen, et al.. Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics. 17th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, Sep 2014, Boston, United States. pp.49 - 56, ⟨10.1007/978-3-319-10443-0_7⟩. ⟨hal-01108237⟩