A deep neural network based correction scheme for improved air-tissue boundary prediction in real-time magnetic resonance imaging video. (March 2021)

• Record Type: Journal Article
• Authors: Mannem, Renuka; Ghosh, Prasanta Kumar
• Is Part Of: Computer speech & language. Volume 66(2021)
• Publication Date: 2021-03
• Subjects: Real-time magnetic resonance imaging video -- Air tissue boundary segmentation -- Deep neural network -- Error correction; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2020.101160
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We propose a deep neural network (DNN) based correction scheme for improved air-tissue boundary (ATB) prediction in real-time magnetic resonance imaging (rtMRI) video. For DNN training, input and target outputs are generated using a normal-grid based method. Experimental study to systematically analyse the DNN based correction scheme for predicted ATBs from various ATB segmentation approaches. Experimental validation of performance of the DNN based correction scheme. Abstract: The real-time Magnetic Resonance Imaging (rtMRI) video captures the vocal tract movements in the mid-sagittal plane during speech. Air tissue boundaries (ATBs) are contours that trace the transition between the high-intensity tissue corresponding to the speech articulators and the low-intensity airway cavity in the rtMRI video. The ATB segmentation in an rtMRI video is a common preprocessing step which is used for many speech production and speech processing applications. However, ATB segmentation is very challenging due to the low resolution and low signal-to-noise ratio of the rtMRI images. Several works have been proposed in the literature for accurate ATB segmentation. However, every ATB segmentation technique, be it knowledge-based or data-driven, has its own limitations due to model assumption or data quality. The errors in the predicted ATBs from a typical ATB segmentation approach can be corrected in a data-driven manner as a post-processing step. In this work, we propose a deep neural network (DNN) based correction scheme for improving the ATB segmentation. In the DNN based correction approach, the correction of each point on a predicted ATB is done using a pattern of intensity variation in the direction of the normal to the predicted ATB at that point. For this, inputs and target outputs needed for DNN training are generated using a normal-grid based method. Experimental results show that the proposed DNN based correction yields more accurate ATBs in terms of Dynamic Time Warping (DTW) distance compared to the ATB segmentation approaches it is applied on. Thus, the DNN based correction could be used as a post-processing step to improve the accuracy of the predicted ATBs from any segmentation scheme.