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Durrleman S., Fletcher T., Gerig G., Niethammer M., Pennec X. (eds.) Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data

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Durrleman S., Fletcher T., Gerig G., Niethammer M., Pennec X. (eds.) Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data
Springer, 2015. — 97 p.
Third International Workshop, STIA 2014. Held in Conjunction with MICCAI 2014. Boston, MA, USA, September 18, 2014. Revised Selected Papers.
The analysis of spatio temporal time-series and longitudinal data is becoming increasingly more important as clinical imaging makes more and more use of longitudinal image studies to examine subject-specific changes due to pathology, intervention, therapy, neurodevelopment, or neurodegeneration. Moreover, dynamic organ changes as seen in cardiac imaging or functional changes as measured in perfusion imaging, just to name a few, by definition result in time-series image data presenting volumetric image data over time.
The detection and characterization of changes from baseline due to disease, trauma, or treatment require novel image processing and visualization tools for qualitative and quantitative assessment of change trajectories. Whereas longitudinal analysis of scalar data is well known in the statistics community, its extension to high-dimensional image data, shapes, or functional changes poses significant challenges. Cross-sectional analysis of longitudinal data does not provide a model of growth or change that considers the inherent correlation of repeated images of individuals, nor does it tell us how an individual patient changes relative to a change over time of a comparable healthy or disease-specific population, an aspect which is highly relevant to decision making and therapy planning.
The goal of this workshop was to comprehensively discuss approaches and new advances for the spatio temporal analysis of time-series and longitudinal image data. It also aimed at starting a dialog to define the generic nature of algorithms, methods, modeling approaches, and statistical analysis for optimal analysis of such data, in particular in the context of challenging applications. The submissions were largely concerned with neuroimaging applications.
Longitudinal Registration and Shape Modeling
Prefrontal Cortical Folding of the Preterm Brain: A Longitudinal Analysis of Preterm-Born Neonates
A Locally Linear Method for Enforcing Temporal Smoothness in Serial Image Registration
Longitudinal Modeling
Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration
Is It Possible to Differentiate the Impact of Pediatric Monophasic Demyelinating Disorders and Multiple Sclerosis After a First Episode of Demyelination?
Joint Longitudinal Modeling of Brain Appearance in Multimodal MRI for the Characterization of Early Brain Developmental Processes
Reconstruction from Longitudinal Data
Longitudinal Guided Super-Resolution Reconstruction of Neonatal Brain MR Images
4D Image Processing
Using the Fourth Dimension to Distinguish Between Structures for Anisotropic Diffusion Filtering in 4D CT Perfusion Scans
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