Dataset Information
A segmentation-informed deep learning framework to register dynamic two-dimensional magnetic resonance images of the vocal tract during speech.
Ontology highlight
ABSTRACT: Objective
Dynamic magnetic resonance (MR) imaging enables visualisation of articulators during speech. There is growing interest in quantifying articulator motion in two-dimensional MR images of the vocal tract, to better understand speech production and potentially inform patient management decisions. Image registration is an established way to achieve this quantification. Recently, segmentation-informed deformable registration frameworks have been developed and have achieved state-of-the-art accuracy. This work aims to adapt such a framework and optimise it for estimating displacement fields between dynamic two-dimensional MR images of the vocal tract during speech.Methods
A deep-learning-based registration framework was developed and compared with current state-of-the-art registration methods and frameworks (two traditional methods and three deep-learning-based frameworks, two of which are segmentation informed). The accuracy of the methods and frameworks was evaluated using the Dice coefficient (DSC), average surface distance (ASD) and a metric based on velopharyngeal closure. The metric evaluated if the fields captured a clinically relevant and quantifiable aspect of articulator motion.Results
The segmentation-informed frameworks achieved higher DSCs and lower ASDs and captured more velopharyngeal closures than the traditional methods and the framework that was not segmentation informed. All segmentation-informed frameworks achieved similar DSCs and ASDs. However, the proposed framework captured the most velopharyngeal closures.Conclusions
A framework was successfully developed and found to more accurately estimate articulator motion than five current state-of-the-art methods and frameworks.Significance
The first deep-learning-based framework specifically for registering dynamic two-dimensional MR images of the vocal tract during speech has been developed and evaluated.
SUBMITTER: Ruthven M
PROVIDER: S-EPMC9746295 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
Publications
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 20230201
<h4>Objective</h4>Dynamic magnetic resonance (MR) imaging enables visualisation of articulators during speech. There is growing interest in quantifying articulator motion in two-dimensional MR images of the vocal tract, to better understand speech production and potentially inform patient management decisions. Image registration is an established way to achieve this quantification. Recently, segmentation-informed deformable registration frameworks have been developed and have achieved state-of-t ...[more]
