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

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Durrleman S., Fletcher T., Gerig G., Niethammer M. (eds.) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Springer, 2015. — 174 p.
Second InternationalWorkshop, STIA 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012 Proceedings.
The second International Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA 2012) was held in Nice, France on October 1st, 2012 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). This workshop was a follow-up of the first international workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA 2010) held in conjunction with MICCAI 2010 in Beijing, which followed a previous tutorial on “Detection and Quantification of Evolving Processes in Medical Images”, organized by Nicholas Ayache at MICCAI 2004.
The analysis of spatio-temporal time-series and longitudinal data is becoming increasingly more important as clinical imaging increasingly makes 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 main body of submissions to the workshop was concerned with neuroimaging applications. However, the workshop also touched on spatio-temporal analysis in biology.
Longitudinal Registration and Transport
Spatio-temporal Regularization for Longitudinal Registration to an Unbiased 3D Individual Template
Local vs Global Descriptors of Hippocampus Shape Evolution for Alzheimer’s Longitudinal Population Analysis
Which Reorientation Framework for the Atlas-Based Comparison of Motion from Cardiac Image Sequences?
Elastic Demons: Characterizing Cortical Development in Neonates Using an Implicit Surface Registration
A New Framework for Analyzing Structural Volume Changes of Longitudinal Brain MRI Data
4D Segmentation of Longitudinal Brain MR Images with Consistent Cortical Thickness Measurement
Spatio-temporal Analysis for Shapes
Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy
Unsupervised Learning of Shape Complexity: Application to Brain Development
Spatio-temporal Analysis under Appearance Changes
Spatial-temporal Pharmacokinetic Model Based Registration of 4D Brain PET Data
Predicting the Location of Glioma Recurrence after a Resection Surgery
Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling
Spatio-temporal Analysis for Biology
Motion-Based Segmentation for Cardiomyocyte Characterization
Multi-temporal Globally-Optimal Dense 3-D Cell Segmentation and Tracking from Multi-photon Time-lapse Movies of Live Tissue Microenvironments
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