轻量级模型实现实时舌部轮廓分割，适用于多种语言与成像条件。
UltraUNet: Real-Time Ultrasound Tongue Segmentation for Diverse Linguistic and Imaging Conditions
- 采用轻量化结构与领域优化设计，降低计算与内存开销。
- 达250帧/秒，单数据集Dice为0.855，跨数据集平均达0.734。
- 适合语音研究、临床诊断及言语运动障碍分析场景。
超声舌成像（UTI）是一种无创且低成本的研究言语发音、运动控制及相关障碍的工具。然而，由于信噪比低、成像差异大及计算需求高，实时舌轮廓分割仍具挑战。本文提出UltraUNet，一种专为实时舌轮廓分割优化的轻量级编码器-解码器架构。其引入领域特异性创新，包括轻量化Squeeze-and-Excitation模块、小批量稳定性保障的组归一化，以及减少内存开销的求和型跳跃连接，并集成去噪与模糊模拟等超声特定增强。在8个数据集上的评估显示，其表现优异：单数据集Dice为0.855，平均均方距离（MSD）为0.993像素；跨数据集平均Dice为0.734和0.761。UltraUNet为语音研究、临床诊断及言语运动障碍分析提供了快速准确的解决方案。
原文摘要 · Abstract (English)
Ultrasound tongue imaging (UTI) is a non-invasive and cost-effective tool for studying speech articulation, motor control, and related disorders. However, real-time tongue contour segmentation remains challenging due to low signal-to-noise ratios, imaging variability, and computational demands. We propose UltraUNet, a lightweight encoder-decoder architecture optimized for real-time segmentation of tongue contours in ultrasound images. UltraUNet incorporates domain-specific innovations such as lightweight Squeeze-and-Excitation blocks, Group Normalization for small-batch stability, and summation-based skip connections to reduce memory and computational overhead. It achieves 250 frames per second and integrates ultrasound-specific augmentations like denoising and blur simulation. Evaluations on 8 datasets demonstrate high accuracy and robustness, with single-dataset Dice = 0.855 and MSD = 0.993px, and cross-dataset Dice averaging 0.734 and 0.761. UltraUNet provides a fast, accurate solution for speech research, clinical diagnostics, and analysis of speech motor disorders.
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