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Roy, A and Belagali, V and Ghosh, PK (2022) AN ERROR CORRECTION SCHEME FOR IMPROVED AIR-TISSUE BOUNDARY IN REAL-TIME MRI VIDEO FOR SPEECH PRODUCTION. In: 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, 23 - 27 May 2022, Virtual, Online at Singapore, pp. 8247-8251.

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Official URL: https://doi.org/10.1109/ICASSP43922.2022.9747876


The best performance in Air-tissue boundary (ATB) segmentation of real-time Magnetic Resonance Imaging (rtMRI) videos in speech production is known to be achieved by a 3-dimensional convolutional neural network (3D-CNN) model. However, the evaluation of this model, as well as other ATB segmentation techniques reported in the literature, is done using Dynamic Time Warping (DTW) distance between the entire original and predicted contours. Such an evaluation measure may not capture local errors in the predicted contour. Careful analysis of predicted contours reveals errors in regions like the velum part of contour1 (ATB comprising of upper lip, hard palate, and velum) and tongue base section of contour2 (ATB covering jawline, lower lip, tongue base, and epiglottis), which are not captured in a global evaluation metric like DTW distance. In this work, we automatically detect such errors and propose a correction scheme for the same. We also propose two new evaluation metrics for ATB segmentation separately in contour1 and contour2 to explicitly capture two types of errors in these contours. The proposed detection and correction strategies result in an improvement of these two evaluation metrics by 61.8 and 61.4 for contour1 and by 67.8 and 28.4 for contour2. Traditional DTW distance, on the other hand, improves by 44.6 for contour1 and 4.0 for contour2. Â

Item Type: Conference Paper
Publication: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: 3D modeling; Convolution; Convolutional neural networks; Error correction; Medical imaging; Tissue, 3-dimensional; 3-dimensional convolutional neural network; Air-tissue boundary segmentation; Boundary segmentation; Convolutional neural network; Real- time; Real-time magnetic resonance imaging video; Tissue boundary; Tongue base; Vela, Magnetic resonance imaging
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 05 Aug 2022 06:45
Last Modified: 05 Aug 2022 06:45
URI: https://eprints.iisc.ac.in/id/eprint/75352

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