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Two step convolutional neural network for automatic glottis localization and segmentation in stroboscopic videos

Belagali, V and Achuth Rao, MV and Gopikishore, P and Krishnamurthy, R and Ghosh, PK (2020) Two step convolutional neural network for automatic glottis localization and segmentation in stroboscopic videos. In: Biomedical Optics Express, 11 (8). pp. 4695-4713.

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Official URL: https://dx.doi.org/10.1364/BOE.396252


Precise analysis of the vocal fold vibratory pattern in a stroboscopic video plays a key role in the evaluation of voice disorders. Automatic glottis segmentation is one of the preliminary steps in such analysis. In this work, it is divided into two subproblems namely, glottis localization and glottis segmentation. A two step convolutional neural network (CNN) approach is proposed for the automatic glottis segmentation. Data augmentation is carried out using two techniques : 1) Blind rotation (WB), 2) Rotation with respect to glottis orientation (WO). The dataset used in this study contains stroboscopic videos of 18 subjects with Sulcus vocalis, in which the glottis region is annotated by three speech language pathologists (SLPs). The proposed two step CNN approach achieves an average localization accuracy of 90.08 and a mean dice score of 0.65.

Item Type: Journal Article
Publication: Biomedical Optics Express
Publisher: OSA - The Optical Society
Additional Information: The copyright of this article belongs to OSA - The Optical Society
Keywords: Convolution, Data augmentation; Localization accuracy; Precise analysis; Speech language pathologists; Sub-problems; Vocal folds; Voice disorders, Convolutional neural networks, adult; article; clinical article; convolutional neural network; female; glottis; human; human experiment; male; rotation; speech language pathologist; videorecording
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 21 Sep 2020 11:23
Last Modified: 21 Sep 2020 11:23
URI: http://eprints.iisc.ac.in/id/eprint/66557

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