Speech-language pathologists (SLPs) are trained to correct articulation of people diagnosed with motor speech disorders by analyzing articulators’ motion and assessing speech outcome while patients speak. To assist SLPs in this task, we are presenting the Multimodal Speech Capture System (MSCS) that records and displays kinematics of key speech articulators, the tongue and lips, along with voice, using unobtrusive methods. Collected speech modalities, tongue motion, lips gestures, and voice, are visualized not only in real-time to provide patients with instant feedback but also offline to allow SLPs to perform post-analysis of articulators’ motion, particularly the tongue, with its prominent but hardly visible role in articulation. We describe the MSCS hardware and software components, and demonstrate its basic visualization capabilities by a healthy individual repeating the words “Hello World”. A proof-of-concept prototype has been successfully developed for this purpose, and will be used in future clinical studies to evaluate its potential impact on accelerating speech rehabilitation by enabling patients to speak as naturally. Pattern matching algorithms to be applied to the collected data can provide patients with quantitative and objective feedback on their speech performance, unlike current methods that are mostly subjective, and may vary from one SLP to another.

Citation: Islam MS, Wang J-K, Johnson SS, Thurtell MJ, Kardon RH, Garvin MK. A deep-learning approach for automated OCT en-face retinal vessel segmentation in cases of optic disc swelling using multiple en-face images as input. Trans Vis Sci Tech. 2020;9(2):17, https://doi.org/10. 1167/tvst.9.2.17 Purpose: In cases of optic disc swelling, segmentation of projected retinal blood vessels from optical coherence tomography (OCT) volumes is challenging due to swellingbased shadowing artifacts. Based on our hypothesis that simultaneously considering vessel information from multiple projected retinal layers can substantially increase vessel visibility, in this work, we propose a deep-learning-based approach to segment vessels involving the simultaneous use of three OCT en-face images as input.Methods: A human expert vessel tracing combining information from OCT en-face images of the retinal pigment epithelium (RPE), inner retina, and total retina as well as a registered fundus image served as the reference standard. The deep neural network was trained from the imaging data from 18 patients with optic disc swelling to output a vessel probability map from three OCT en-face input images. The vessels from the OCT en-face images were also manually traced in three separate stages to compare with the performance of the proposed approach.Results: On an independent volume-matched test set of 18 patients, the proposed deep-learning-based approach outperformed the three OCT-based manual tracing stages. The manual tracing based on three OCT en-face images also outperformed the manual tracing using only the traditional RPE en-face image. Conclusions:In cases of optic disc swelling, use of multiple en-face images enables better vessel segmentation when compared with the traditional use of a single en-face image.Translational Relevance: Improved vessel segmentation approaches in cases of optic disc swelling can be used as features for an improved assessment of the severity and cause of the swelling.

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