Inverse biomechanical modeling of the tongue via machine learning and synthetic training data

Aniket A. Tolpadi; Maureen L. Stone; Aaron Carass; Jerry L. Prince; Arnold D. Gomez

doi:10.1117/12.2296927

12 March 2018 Inverse biomechanical modeling of the tongue via machine learning and synthetic training data

Aniket A. Tolpadi; Maureen L. Stone; Aaron Carass; Jerry L. Prince; Arnold D. Gomez

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Proceedings Volume 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling; 1057606 (2018) https://doi.org/10.1117/12.2296927
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

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Citation Download Citation

Aniket A. Tolpadi, Aniket A. Tolpadi, Maureen L. Stone, Maureen L. Stone, Aaron Carass, Aaron Carass, Jerry L. Prince, Jerry L. Prince, Arnold D. Gomez, Arnold D. Gomez, } "Inverse biomechanical modeling of the tongue via machine learning and synthetic training data", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057606 (12 March 2018); doi: 10.1117/12.2296927; https://doi.org/10.1117/12.2296927

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