Air-Tissue Boundary Segmentation in Real-Time Magnetic Resonance Imaging Video Using Semantic Segmentation with Fully Convolutional Networks

Valliappan, C.A.; Mannem, Renuka; Ghosh, Prasanta Kumar

doi:10.21437/interspeech.2018-1939

Cited by 6 publications

(2 citation statements)

References 28 publications

(29 reference statements)

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“…To avoid excessive interpolation, higher native frame rates could be used; however, image quality tends to worsen at higher frame rates, and this would make the segmentation in the first stage more Artificial-intelligence-based segmentation methods of the upper vocal tract have started to emerge and, in the future, could represent a suitable alternative for segmentation. However, they are only detecting the air-tissue interface [89][90][91], with some also detecting with which organ it is in contact with [92][93][94] and one fully segmenting the airway [95]. However, currently, none fully segment the articulators.…”

Section: Phantom Developmentmentioning

confidence: 99%

Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract

et al. 2020

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Dynamic and real-time MRI (rtMRI) of human speech is an active field of research, with interest from both the linguistics and clinical communities. At present, different research groups are investigating a range of rtMRI acquisition and reconstruction approaches to visualise the speech organs. Similar to other moving organs, it is difficult to create a physical phantom of the speech organs to optimise these approaches; therefore, the optimisation requires extensive scanner access and imaging of volunteers. As previously demonstrated in cardiac imaging, realistic numerical phantoms can be useful tools for optimising rtMRI approaches and reduce reliance on scanner access and imaging volunteers. However, currently, no such speech rtMRI phantom exists. In this work, a numerical phantom for optimising speech rtMRI approaches was developed and tested on different reconstruction schemes. The novel phantom comprised a dynamic image series and corresponding k-space data of a single mid-sagittal slice with a temporal resolution of 30 frames per second (fps). The phantom was developed based on images of a volunteer acquired at a frame rate of 10 fps. The creation of the numerical phantom involved the following steps: image acquisition, image enhancement, segmentation, mask optimisation, through-time and spatial interpolation and finally the derived k-space phantom. The phantom was used to: (1) test different k-space sampling schemes (Cartesian, radial and spiral); (2) create lower frame rate acquisitions by simulating segmented k-space acquisitions; (3) simulate parallel imaging reconstructions (SENSE and GRAPPA). This demonstrated how such a numerical phantom could be used to optimise images and test multiple sampling strategies without extensive scanner access.

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Section: Phantom Developmentmentioning

confidence: 99%

Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract

et al. 2020

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“…Several other methods on semantic segmentation based estimation of vocal tract in MRI frames are proposed using fully convolutional networks [25] and convolutional encoderdecoder architectures [26], [27]. In these works, an MR image is segmented into three separate tissue regions and the VT boundary is obtained in a post-processing stage via an edge detection scheme.…”

Section: Introductionmentioning

confidence: 99%

Vocal Tract Contour Tracking in rtMRI Using Deep Temporal Regression Network

Asadiabadi

Erzin

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Recent advances in real-time Magnetic Resonance Imaging (rtMRI) provide an invaluable tool to study speech articulation. In this paper, we present an effective deep learning approach for supervised detection and tracking of vocal tract contours in a sequence of rtMRI frames. We train a single input multiple output deep temporal regression network (DTRN) to detect the vocal tract (VT) contour and the separation boundary between different articulators. The DTRN learns the non-linear mapping from an overlapping fixed-length sequence of rtMRI frames to the corresponding articulatory movements, where a blend of the overlapping contour estimates defines the detected VT contour. The detected contour is refined at a post-processing stage using an appearance model to further improve the accuracy of VT contour detection. The proposed VT contour tracking model is trained and evaluated over the USC-TIMIT dataset. Performance evaluation is carried out using three objective assessment metrics for the separating landmark detection, contour tracking and temporal stability of the contour landmarks in comparison with three baseline approaches from the recent literature. Results indicate significant improvements with the proposed method over the state-of-the-art baselines.

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Real-time speech MRI datasets with corresponding articulator ground-truth segmentations

Ruthven,

Peplinski,

Adams

et al. 2023

Sci Data

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The use of real-time magnetic resonance imaging (rt-MRI) of speech is increasing in clinical practice and speech science research. Analysis of such images often requires segmentation of articulators and the vocal tract, and the community is turning to deep-learning-based methods to perform this segmentation. While there are publicly available rt-MRI datasets of speech, these do not include ground-truth (GT) segmentations, a key requirement for the development of deep-learning-based segmentation methods. To begin to address this barrier, this work presents rt-MRI speech datasets of five healthy adult volunteers with corresponding GT segmentations and velopharyngeal closure patterns. The images were acquired using standard clinical MRI scanners, coils and sequences to facilitate acquisition of similar images in other centres. The datasets include manually created GT segmentations of six anatomical features including the tongue, soft palate and vocal tract. In addition, this work makes code and instructions to implement a current state-of-the-art deep-learning-based method to segment rt-MRI speech datasets publicly available, thus providing the community and others with a starting point for developing such methods.

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Air-Tissue Boundary Segmentation in Real-Time Magnetic Resonance Imaging Video Using Semantic Segmentation with Fully Convolutional Networks

Cited by 6 publications

References 28 publications

Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract

Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract

Vocal Tract Contour Tracking in rtMRI Using Deep Temporal Regression Network

Real-time speech MRI datasets with corresponding articulator ground-truth segmentations

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