Fishbaugh James, Paniagua Beatriz, Mostapha Mahmoud, Styner Martin, Murphy Veronica, Gilmore John, Gerig Guido
Computer Science and Engineering, Tandon School of Engineering, NYU.
Kitware Inc.
Proc SPIE Int Soc Opt Eng. 2019 Feb;10949. doi: 10.1117/12.2513030. Epub 2019 Mar 15.
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 over fit. 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.
时空形状模型捕捉形状随时间变化的动态，是监测和测量解剖结构生长或退化的重要工具。在本文中，我们评估了非参数形状回归在从出生开始对幼儿皮质下发育进行建模这一具有挑战性的问题上的应用。由于模型的灵活性，选择既能导致良好的模型拟合又不会过度拟合的参数可能具有挑战性。我们系统地测试了各种参数设置，以评估模型拟合以及该方法对特定参数的敏感性，并且我们探讨了缺失数据对模型估计的影响。
