Conference proceeding
Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Vol.8675(3), pp.49-56
Lecture Notes in Computer Science
International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 17th (2014)
2014
DOI: 10.1007/978-3-319-10443-0_7
PMCID: PMC4486086
PMID: 25320781
Abstract
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).
Details
- Title: Subtitle
- Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics
- Creators
- Prasanna Muralidharan - University of UtahJames Fishbaugh - University of UtahHans J Johnson - University of IowaStanley Durrleman - Université Paris CitéJane S Paulsen - University of IowaGuido Gerig - University of UtahThomas P Fletcher - School of computing [UTAH]
- Resource Type
- Conference proceeding
- Publication Details
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Vol.8675(3), pp.49-56
- Conference
- International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 17th (2014)
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-10443-0_7
- PMID
- 25320781
- PMCID
- PMC4486086
- ISSN
- 0302-9743
- eISSN
- 1611-3349
- Publisher
- Springer
- Language
- English
- Date published
- 2014
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Psychological and Brain Sciences
- Record Identifier
- 9984185369102771
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