A framework for longitudinal data analysis via shape regression

Abstract : Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.
Type de document :
Communication dans un congrès
David Haynor and Sébastien Ourselin. SPIE - Medical Imaging 2012: Image Processing, Feb 2012, San Diego, United States. SPIE, 8314, 2012, 〈10.1117/12.911721〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00818428
Contributeur : Stanley Durrleman <>
Soumis le : vendredi 26 avril 2013 - 18:57:01
Dernière modification le : vendredi 23 novembre 2018 - 08:54:35

Lien texte intégral

Identifiants

Collections

Citation

James Fishbaugh, Stanley Durrleman, Joseph Piven, Guido Gerig. A framework for longitudinal data analysis via shape regression. David Haynor and Sébastien Ourselin. SPIE - Medical Imaging 2012: Image Processing, Feb 2012, San Diego, United States. SPIE, 8314, 2012, 〈10.1117/12.911721〉. 〈hal-00818428〉

Partager

Métriques

Consultations de la notice

197