Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not overfit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation.
James Fishbaugh, Beatriz Paniagua, Mahmoud Mostapha, Martin Styner, Veronica Murphy, John Gilmore, and Guido Gerig, "Model selection for spatiotemporal modeling of early childhood sub-cortical development," Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490L (Presented at SPIE Medical Imaging: February 20, 2019; Published: 15 March 2019); https://doi.org/10.1117/12.2513030.
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