Spatial nonparametric mixed-effects model with spatial-varying coefficients for analysis of populations

Juan David Ospina*, Oscar Acosta, Gaël Dréan, Guillaume Cazoulat, Antoine Simon, Juan Carlos Correa, Pascal Haigron, Renaud De Crevoisier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Voxel-wise comparisons have been largely used in the analysis of populations to identify biomarkers for pathologies, disease progression, or to predict a treatment outcome. On the basis of a good interindividual spatial alignment, 3D maps are produced, allowing to localise regions where significant differences between groups exist. However, these techniques have received some criticism as they rely on conditions wich are not always met. Firstly, the results may be affected by misregistrations; secondly, the statistics behind the models assumes normally distributed data; finally, because of the size of the images, some strategies must be used to control for the rate of false detection. In this paper, we propose a spatial (3D) nonparametric mixed-effects model for population analysis. It overcomes some of the issues of classical voxel-based approaches, namely robustness to false positive rates, misregistrations and large variances between groups. Examples on numerical phantoms and real clinical data illustrate the feasiblity of the approach. An example of application within the development of voxel-wise predictive models of rectal toxicity in prostate cancer radiotherapy is presented. Results demonstrate an improved sensitivity and reliability for group analysis compared with standard voxel-wise methods and open the way for potential applications in computational anatomy.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
PublisherSpringer Verlag
Pages142-150
Number of pages9
ISBN (Print)9783642243189
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 18 Sept 201118 Sept 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
Country/TerritoryCanada
CityToronto, ON
Period18/9/1118/9/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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