Tolpadi Aniket A, Stone Maureen L, Carass Aaron, Prince Jerry L, Gomez Arnold D
Department of Bioengineering, Rice University, Houston, TX, US 77005.
Department of Neural and Pain Sciences, Dept of Orthodontics, University of Maryland Dental School, Baltimore, MD, US 21201.
Proc SPIE Int Soc Opt Eng. 2018 Feb;10576. doi: 10.1117/12.2296927. Epub 2018 Mar 12.
The tongue's deformation during speech can be measured using tagged magnetic resonance imaging, but there is no current method to directly measure the pattern of muscles that activate to produce a given motion. In this paper, the activation pattern of the tongue's muscles is estimated by solving an inverse problem using a random forest. Examples describing different activation patterns and the resulting deformations are generated using a finite-element model of the tongue. These examples form training data for a random forest comprising 30 decision trees to estimate contractions in 262 contractile elements. The method was evaluated on data from tagged magnetic resonance data from actual speech and on simulated data mimicking flaps that might have resulted from glossectomy surgery. The estimation accuracy was modest (5.6% error), but it surpassed a semi-manual approach (8.1% error). The results suggest that a machine learning approach to contraction pattern estimation in the tongue is feasible, even in the presence of flaps.
在言语过程中舌头的变形可以通过标记磁共振成像来测量，但目前尚无直接测量激活以产生给定运动的肌肉模式的方法。在本文中，通过使用随机森林解决逆问题来估计舌头肌肉的激活模式。使用舌头的有限元模型生成描述不同激活模式和由此产生的变形的示例。这些示例构成了一个由30个决策树组成的随机森林的训练数据，以估计262个收缩元件中的收缩情况。该方法在来自实际言语的标记磁共振数据以及模拟舌切除术可能导致的皮瓣的数据上进行了评估。估计精度适中（误差为5.6%），但超过了半手动方法（误差为8.1%）。结果表明，即使存在皮瓣，机器学习方法用于估计舌头的收缩模式也是可行的。
