双协同训练：极端数据稀缺场景下的跨数据集超声舌部分割
Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity
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- Hong Kong Polytechnic University（香港理工大学）
- Research Institute for Smart Ageing（智能老龄化研究院）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出基于UltraUNet的无源域适应框架，通过伪标签优化、质量控制及条件GAN合成样本，在12组跨数据集超声舌分割任务中优于基线方法，提升了分割性能。
中文摘要 AI 辅助
在跨数据集域偏移场景下，超声舌轮廓分割仍具挑战性，有限标注、探头差异及采集噪声常降低模型泛化能力。本文提出一种用于鲁棒超声舌部分割的无源域适应框架，基于轻量型UltraUNet骨干网络构建。从仅用5张标注源图像预训练的检查点（模拟欠拟合的受限源模型）出发，所提方法通过迭代优化伪标签、采用基于轮廓的质量控制模块过滤不可靠掩码、借助分割引导的条件GAN生成目标风格的合成图像-掩码对，以适配完全未标注的目标域。随后，在干净的伪标注目标图像、带一致性正则化的含噪伪标签及合成样本的混合数据上训练学生模型，实现无需访问源数据的闭环适应。我们在8个超声舌成像数据集的12组源-目标迁移对上评估该方法，并开展源规模缩放实验与消融研究。在所有比较中，所提框架较包括监督模型在内的基线方法提升了分割重叠度与轮廓精度。这些结果表明，针对超声舌成像任务的特定伪标签优化及目标风格合成增强可显著改善无源适应效果。
英文摘要
Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisition noise often degrade model generalization. We present a source-free domain adaptation framework for robust ultrasound tongue segmentation built on a lightweight UltraUNet backbone. Starting from a checkpoint pretrained on only five labeled source images, simulating an underfitted constrained source model, the proposed method adapts to a fully-unlabeled target domain by iteratively refining pseudo-labels, filtering unreliable masks with a contour-based quality-control module, and generating target-style synthetic image-mask pairs through a segmentation-guided conditional GAN. The student model is then trained on a mixture of clean pseudo-labeled target images, noisy pseudo-labels with consistency regularization, and synthetic samples, enabling closed-loop adaptation without access to source data. We evaluate the method on 12 source-target transfer pairs across eight ultrasound tongue imaging datasets, and conduct source-size scaling experiments and ablation studies. Across all comparisons, the proposed framework improves segmentation overlap and contour accuracy over the baselines, including supervised ones. These results suggest that task-specific pseudo-label refinement and synthetic target-style augmentation can substantially improve source-free adaptation for ultrasound tongue imaging.
