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Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning

Subin Erattakulangara¹, Karthika Kelat¹, David Meyer²

¹Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA.

Bioengineering (Basel, Switzerland)

|May 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel stacked transfer learning U-NET model for segmenting the vocal tract in dynamic speech MRI scans. The model accurately analyzes speech production by leveraging pre-trained features, achieving expert-level segmentation with minimal protocol-specific data.

Keywords:

articulator segmentation dynamic speech MRI protocol adaptiveness transfer learning

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Speech Science

Background:

Dynamic magnetic resonance imaging (MRI) is crucial for understanding speech production by analyzing vocal tract airspace and articulator movement.
Fast speech MRI protocols generate high-frame-rate datasets (80-100 fps), enabling detailed functional analysis.
Accurate segmentation of the deforming vocal tract in these dynamic images is essential for quantitative analysis.

Purpose of the Study:

To propose and evaluate a stacked transfer learning U-NET model for segmenting the vocal tract in 2D mid-sagittal dynamic speech MRI slices.
To demonstrate the model's adaptability across different fast speech MRI protocols with limited labeled data.
To compare the model's segmentation performance against expert human annotators and a conventional U-NET model.

Main Methods:

A stacked transfer learning U-NET architecture was developed, integrating low-, mid-, and high-level features.
Low- and mid-level features were initialized using models pre-trained on brain tumor MR, lung CT, and airway datasets.
High-level features were fine-tuned on protocol-specific dynamic speech MRI data from three distinct acquisition protocols.

Main Results:

The proposed model achieved accurate vocal tract segmentations comparable to expert human annotators.
Performance was validated across three different fast speech MRI protocols (radial, uniform spiral, variable density spiral) with varying regularization techniques.
The transfer learning approach required only a small number of protocol-specific images (approx. 20) for successful adaptation.

Conclusions:

Stacked transfer learning offers an effective method for segmenting dynamic speech MRI data, even with limited labeled samples.
The model demonstrates robustness and adaptability to diverse speech MRI acquisition protocols and speech tasks.
This approach significantly enhances the ability to analyze vocal tract dynamics during speech production using MRI.