Skip to Main content Skip to Navigation
Journal articles

Learning the spatiotemporal variability in longitudinal shape data sets

Alexandre Bône 1 Olivier Colliot 1 Stanley Durrleman 1
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : In this paper, we propose a generative statistical model to learn the spatiotemporal variability in longitudinal shape data sets, which contain repeated observations of a set of objects or individuals over time. From all the short-term sequences of individual data, the method estimates a long-term normative scenario of shape changes and a tubular coordinate system around this trajectory. Each individual data sequence is therefore (i) mapped onto a specific portion of the trajectory accounting for differences in pace of progression across individuals, and (ii) shifted in the shape space to account for intrinsic shape differences across individuals that are independent of the progression of the observed process. The parameters of the model are estimated using a stochastic approximation of the expectation–maximization algorithm. The proposed approach is validated on a simulated data set, illustrated on the analysis of facial expression in video sequences, and applied to the modeling of the progressive atrophy of the hippocampus in Alzheimer’s disease patients. These experiments show that one can use the method to reconstruct data at the precision of the noise, to highlight significant factors that may modulate the progression, and to simulate entirely synthetic longitudinal data sets reproducing the variability of the observed process.
Complete list of metadata

Cited literature [65 references]  Display  Hide  Download

https://hal.inria.fr/hal-02091549
Contributor : Alexandre Bône <>
Submitted on : Friday, July 3, 2020 - 11:04:12 AM
Last modification on : Monday, April 5, 2021 - 5:12:02 PM

File

IJCV.pdf
Files produced by the author(s)

Identifiers

Citation

Alexandre Bône, Olivier Colliot, Stanley Durrleman. Learning the spatiotemporal variability in longitudinal shape data sets. International Journal of Computer Vision, Springer Verlag, 2020, ⟨10.1007/s11263-020-01343-w⟩. ⟨hal-02091549v5⟩

Share

Metrics

Record views

137

Files downloads

815