Registration of 4D Time-Series of Cardiac Images with Multichannel Diffeomorphic Demons
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Large Diffeomorphic FFD Registration for Motion and Strain Quantification from 3D-US Sequences
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Generation of a mean motion model of the lung using 4D-CT image data
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
A Reparameterisation Based Approach to Geodesic Constrained Solvers for Curve Matching
International Journal of Computer Vision
Simple geodesic regression for image time-series
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Analysis of longitudinal shape variability via subject specific growth modeling
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Mixed-Effects shape models for estimating longitudinal changes in anatomy
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Geodesic shape regression in the framework of currents
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.