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Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics

Institution:
1School of Computing & SCI Institute, University of Utah, UT, USA.
2Department of Psychiatry, Carver College of Medicine, University of Iowa, IA, USA.
3ICM, Hôpital de la Pitié Salpêtrière, Paris, France.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
17
Issue Number:
Pt 3
Pages:
49-56
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):49-56.
PubMed ID:
25320781
PMCID:
PMC4486086
Appears in Collections:
NA-MIC
Sponsors:
R01 NS040068/NS/NINDS NIH HHS/United States
R01 NS054893/NS/NINDS NIH HHS/United States
R01 NS050568/NS/NINDS NIH HHS/United States
S10 RR023392/RR/NCRR NIH HHS/United States
U01 NS082086/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Muralidharan P., Fishbaugh J., Johnson H.J., Durrleman S., Paulsen J.S., Gerig G., Fletcher P.T. Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics. Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):49-56. PMID: 25320781. PMCID: PMC4486086.
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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).

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