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

Aniket A. Tolpadi, Maureen L. Stone, Aaron Carass, Jerry Ladd Prince, Arnold Gomez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Robert J. Webster
PublisherSPIE
Volume10576
ISBN (Electronic)9781510616417
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Other

OtherMedical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CityHouston
Period2/12/182/15/18

Keywords

  • Biomechanics
  • Machine-Learning
  • Model Inversion
  • Random Forest

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Tolpadi, A. A., Stone, M. L., Carass, A., Prince, J. L., & Gomez, A. (2018). Inverse biomechanical modeling of the tongue via machine learning and synthetic training data. In B. Fei, & R. J. Webster (Eds.), Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10576). [1057606] SPIE. https://doi.org/10.1117/12.2296927