biostudies-literatureUnknown80Ruthven M<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-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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>A framework was successfully developed and found to more accurately estimate articulator motion than five current state-of-the-art methods and frameworks.<h4>Significance</h4>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.Biomedical signal processing and control104290https://www.ebi.ac.uk/biostudies/studies/S-EPMC9746295biostudies-literatureA segmentation-informed deep learning framework to register dynamic two-dimensional magnetic resonance images of the vocal tract during speech.PMC9746295King APMiquel MERuthven MfalseA segmentation-informed deep learning framework to register dynamic two-dimensional magnetic resonance images of the vocal tract during speech.<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-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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>A framework was successfully developed and found to more accurately estimate articulator motion than five current state-of-the-art methods and frameworks.<h4>Significance</h4>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.2023-01-01T00:00:00Z2023 Feb2025-04-05T13:30:22.688Z2025-04-05T13:30:22.688ZS-EPMC97462953674369910.1016/j.bspc.2022.104290