Inverse Biomechanical Modeling of the Tongue via Machine Learning and Synthetic Training Data.
Inverse Biomechanical Modeling of the Tongue via Machine Learning and Synthetic Training Data.
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通过机器学习和综合训练数据对舌头进行逆向生物力学建模。
DOI：
10.1117/12.2296927
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Gomez,ArnoldD
中科院分区：
文献类型：
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作者：
Tolpadi,AniketA;Stone,MaureenL;Carass,Aaron;Prince,JerryL;Gomez,ArnoldD
舌头在说话过程中的变形可以使用标记磁共振成像来测量，但目前还没有直接测量肌肉模式的方法，这些肌肉被激活以产生给定的运动。在这篇文章中，舌头肌肉的激活模式是通过使用随机森林求解一个反问题来估计的。使用舌头的有限元模型生成描述不同激活模式和所产生的变形的实例。这些例子形成了由30个决策树组成的随机森林的训练数据，以估计262个收缩元素的收缩。该方法是基于来自实际语音的标记磁共振数据和模拟可能来自舌叶切除手术的瓣的模拟数据来评估的。估计精度不高(5.6%的误差)，但超过了半手工方法(8.1%的误差)。结果表明，即使在有瓣的情况下，用机器学习的方法来估计舌部的收缩模式是可行的。
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 semimanual 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.