Air-tissue Boundary Segmentation in Real Time Magnetic Resonance Imaging Video Using a Convolutional Encoder-decoder Network

Mannem, R and Ghosh, PK (2019) Air-tissue Boundary Segmentation in Real Time Magnetic Resonance Imaging Video Using a Convolutional Encoder-decoder Network. In: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, 12 - 17 May 2019, Brighton, pp. 5941-5945.

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Abstract

In this paper, we propose a convolutional encoder-decoder network (CEDN) based approach for upper and lower Air-Tissue Boundary (ATB) segmentation within vocal tract in real-time magnetic resonance imaging (rtMRI) video frames. The output images from CEDN are processed using perimeter and moving average filters to generate smooth contours representing ATBs. Experiments are performed in both seen subject and unseen subject conditions to examine the generalizability of the CEDN based approach. The performance of the segmented ATBs is evaluated using Dynamic Time Warping distance between the ground truth contours and predicted contours. The proposed approach is compared with three baseline schemes - one grid-based unsupervised and two supervised schemes. Experiments with 5779 rtMRI images from four subjects show that the CEDN based approach performs better than the unsupervised baseline scheme by 8.5 for seen subjects case whereas it does better than the supervised baseline schemes only for lower ATB. For unseen subjects case, the proposed approach performs better than the supervised baseline schemes by 63.96, 22.9 respectively whereas it performs worse than the unsupervised baseline scheme. However, the proposed approach outperforms the unsupervised baseline scheme when a minimum of 30 images from unseen subjects are used to adapt the trained CEDN model. © 2019 IEEE.

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 Institute of Electrical and Electronics Engineers Inc.
Keywords:	Audio signal processing; Convolution; Decoding; Magnetic resonance imaging; Magnetism; Resonance; Speech communication; Tissue; Tissue engineering, Convolutional encoders; Dynamic time warping distance; Ground truth; Moving average filter; Real time; Tissue boundary; Video frame; Vocal-tracts, Network coding
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	28 Dec 2022 05:53
Last Modified:	28 Dec 2022 05:53
URI:	https://eprints.iisc.ac.in/id/eprint/78598

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