Comparison of auto-contouring and hand-contouring of ultrasound images of the tongue surface

Contours traced by trained phoneticians have been considered to be the most accurate way to identify the midsagittal tongue surface from ultrasound video frames. In this study, inter-measurer reliability was evaluated using measures that quantified both how closely human-placed contours approximated each other as well as how consistent measurers were in defining the start and end points of contours. High reliability across three measurers was found for all measures, consistent with treating contours placed by trained phoneticians as the ‘gold standard.’ However, due to the labour-intensive nature of hand-placing contours, automatic algorithms that detect the tongue surface are increasingly being used to extract tongue-surface data from ultrasound videos. Contours placed by six automatic algorithms (SLURP, EdgeTrak, EPCS, and three different configurations of the algorithm provided in Articulate Assistant Advanced) were compared to human-placed contours, with the same measures used to evaluate the consistency of the trained phoneticians. We found that contours defined by SLURP, EdgeTrak, and two of the AAA configurations closely matched the hand-placed contours along sections of the image where the algorithms and humans agreed that there was a discernible contour. All of the algorithms were much less reliable than humans in determining the anterior (tongue-tip) edge of tongue contours. Overall, the contours produced by SLURP, EdgeTrak, and AAA should be useable in a variety of clinical applications, subject to spot-checking. Additional practical considerations of these algorithms are also discussed.