Automated measurement of geniohyoid muscle thickness during speech using deep learning and ultrasound

Abstract

Manual measurement of muscle morphology from ultrasound during speech is time-consuming and limits large-scale studies. We present SMMA, a fully automated framework combining deep learning segmentation with skeleton-based thickness quantification for analyzing geniohyoid (GH) muscle dynamics. Validation demonstrates near-human-level accuracy (Dice: 0.9037, MAE: 0.53 mm, r = 0.901). Application to Cantonese vowel production (N = 11) reveals systematic patterns: /a:/ shows significantly greater GH thickness (7.29 mm) than /i:/ (5.95 mm, p < 0.001, Cohen's d > 1.3), suggesting greater GH activation during the production of /a:/ than /i:/, consistent with its role in mandibular depression. Sex differences (5-8% greater in males) reflect anatomical scaling. SMMA achieves expert-validated accuracy while eliminating the need for manual annotation, enabling scalable investigations of speech motor control and objective assessment of speech/swallowing disorders.