用深度学习自动测量说话时舌骨肌厚度，提升研究效率。
Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound
- 用深度学习分割+骨架法量化肌肉厚度，全自动分析。
- 对粤语元音发音测试显示，/a:/比/i:/厚1.34毫米，差异显著。
- 适合语音运动控制、吞咽障碍等临床研究，无需人工标注。
手动从说话时的超声图像中测量肌肉形态耗时长，限制大规模研究。本文提出SMMA框架，结合深度学习分割与基于骨架的厚度量化方法，分析舌骨肌（GH）动态变化。验证显示接近人类专家水平的准确性（Dice=0.9037，MAE=0.53 mm，r=0.901）。在11名受试者粤语元音发音分析中发现系统性模式：/a:/时GH厚度达7.29毫米，显著大于/i:/的5.95毫米（p<0.001，Cohen's d>1.3），表明/a:/发音时舌骨肌激活更强，与下颌下降功能一致。男性较女性大5%-8%，反映解剖尺度差异。SMMA实现专家级精度，无需人工标注，支持语音运动控制的大规模研究及言语吞咽障碍的客观评估。
原文摘要 · Abstract (English)
Manual measurement of muscle morphology from ultrasound during speech is time-consuming and limits large-scale studies. We present SMMA, a fully automated framework that combines deep-learning segmentation with skeleton-based thickness quantification to analyze 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 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 and swallowing disorders.
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