TY - GEN
T1 - Analysis of longitudinal shape variability via subject specific growth modeling
AU - Fishbaugh, James
AU - Prastawa, Marcel
AU - Durrleman, Stanley
AU - Piven, Joseph
AU - Gerig, Guido
AU - IBIS Network, Network
N1 - Funding Information:
Acknowledgments. This work was supported by NIH grant RO1 HD055741 (ACE, project IBIS) and by NIH grant U54 EB005149 (NA-MIC).
Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
PY - 2012
Y1 - 2012
N2 - Statistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. We propose a new approach for analyzing shape variability over time, and for quantifying spatiotemporal population differences. Our approach estimates 4D anatomical growth models for a reference population (an average model) and for individuals in different groups. We define a reference 4D space for our analysis as the average population model and measure shape variability through diffeomorphisms that map the reference to the individuals. Conducting our analysis on this 4D space enables straightforward statistical analysis of deformations as they are parameterized by momenta vectors that are located at homologous locations in space and time. We evaluate our method on a synthetic shape database and clinical data from a study that seeks to quantify brain growth differences in infants at risk for autism.
AB - Statistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. We propose a new approach for analyzing shape variability over time, and for quantifying spatiotemporal population differences. Our approach estimates 4D anatomical growth models for a reference population (an average model) and for individuals in different groups. We define a reference 4D space for our analysis as the average population model and measure shape variability through diffeomorphisms that map the reference to the individuals. Conducting our analysis on this 4D space enables straightforward statistical analysis of deformations as they are parameterized by momenta vectors that are located at homologous locations in space and time. We evaluate our method on a synthetic shape database and clinical data from a study that seeks to quantify brain growth differences in infants at risk for autism.
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U2 - 10.1007/978-3-642-33415-3_90
DO - 10.1007/978-3-642-33415-3_90
M3 - Conference contribution
C2 - 23285617
AN - SCOPUS:84872556018
SN - 9783642334146
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 731
EP - 738
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
A2 - Ayache, Nicholas
A2 - Delingette, Herve
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer Verlag
T2 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 5 October 2012
ER -