ArticleComputer methods and programs in biomedicine2021
Deep-learning-based segmentation of the vocal tract and articulators in real-time magnetic resonance images of speech.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- GoLoCo-Net: global-local guided contextual attention network for medical images segmentation.Scientific reports · 2026Article
- PRORED: a hybrid transformer framework with progressive refinement decoding for segmenting dynamic speech MRI.BJR artificial intelligence · 2026Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks.Journal of voice : official journal of the Voice Foundation · 2025Article
- An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images.Behavior research methods · 2024Article
- Real-time speech MRI datasets with corresponding articulator ground-truth segmentations.Scientific data · 2023Article
- Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model.Bioengineering (Basel, Switzerland) · 2023Article
- A segmentation-informed deep learning framework to register dynamic two-dimensional magnetic resonance images of the vocal tract during speech.Biomedical signal processing and control · 2023Article
- Tongue model construction based on ultrasound images with image processing and deep learning method.Journal of medical ultrasonics (2001) · 2022Article
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Abstract
BACKGROUND AND
objectiveMagnetic resonance (MR) imaging is increasingly used in studies of speech as it enables non-invasive visualisation of the vocal tract and articulators, thus providing information about their shape, size, motion and position. Extraction of this information for quantitative analysis is achieved using segmentation. Methods have been developed to segment the vocal tract, however, none of these also fully segment any articulators. The objective of this work was to develop a method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech, thus overcoming the limitations of existing methods.
methodsFive speech MR image sets (392 MR images in total), each of a different healthy adult volunteer, were used in this work. A fully convolutional network with an architecture similar to the original U-Net was developed to segment the following six regions in the image sets: the head, soft palate, jaw, tongue, vocal tract and tooth space. A five-fold cross-validation was performed to investigate the segmentation accuracy and generalisability of the network. The segmentation accuracy was assessed using standard overlap-based metrics (Dice coefficient and general Hausdorff distance) and a novel clinically relevant metric based on velopharyngeal closure.
resultsThe segmentations created by the method had a median Dice coefficient of 0.92 and a median general Hausdorff distance of 5mm. The method segmented the head most accurately (median Dice coefficient of 0.99), and the soft palate and tooth space least accurately (median Dice coefficients of 0.92 and 0.93 respectively). The segmentations created by the method correctly showed 90% (27 out of 30) of the velopharyngeal closures in the MR image sets.
conclusionsAn automatic method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech was successfully developed. The method is intended for use in clinical and non-clinical speech studies which involve quantitative analysis of the shape, size, motion and position of the vocal tract and articulators. In addition, a novel clinically relevant metric for assessing the accuracy of vocal tract and articulator segmentation methods was developed.
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