@inproceedings{7b503060138f40b1b58dfac4f69d91e2,
title = "Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment",
abstract = "This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.",
keywords = "Mallow's distance, diffusion tensor imaging, distribution-valued data, early neurodevelopment, spatiotemporal growth trajectory",
author = "Anuja Sharma and Fletcher, {P. Thomas} and Gilmore, {John H.} and Escolar, {Maria L.} and Avantika Vardhan and Aditya Gupta and Martin Styner and Guido Gerig",
year = "2013",
doi = "10.1109/ISBI.2013.6556567",
language = "English (US)",
isbn = "9781467364546",
series = "Proceedings - International Symposium on Biomedical Imaging",
pages = "684--687",
booktitle = "ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging",
note = "2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 ; Conference date: 07-04-2013 Through 11-04-2013",
}