统一处理多种超声器官分割，用提示引导实现高效半监督学习
ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling
- 通过提示引导的双解码器结构，灵活适应不同分割任务
- 在5个器官8项任务上超越现有方法，提升显著
- 适合需要通用超声分析能力的临床研究与系统开发
现有超声图像分割方法多针对特定解剖结构或任务设计，限制了其在临床中的实际应用。本文首次提出通用半监督超声图像分割任务，并提出ProPL框架，可同时处理多个器官和分割任务，充分利用标注与未标注数据。该框架采用共享视觉编码器与提示引导的双解码器，通过提示-解码机制实现灵活任务适配，并借助不确定性驱动的伪标签校准（UPLC）模块实现可靠自训练。为推动该方向研究，我们构建了一个涵盖5个器官、8项分割任务的综合性超声数据集。大量实验表明，ProPL在多个指标上均优于现有最先进方法，建立了通用超声图像分割的新基准。
原文摘要 · Abstract (English)
Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks, limiting their practical utility in clinical settings. In this paper, we pioneer the task of universal semi-supervised ultrasound image segmentation and propose ProPL, a framework that can handle multiple organs and segmentation tasks while leveraging both labeled and unlabeled data. At its core, ProPL employs a shared vision encoder coupled with prompt-guided dual decoders, enabling flexible task adaptation through a prompting-upon-decoding mechanism and reliable self-training via an uncertainty-driven pseudo-label calibration (UPLC) module. To facilitate research in this direction, we introduce a comprehensive ultrasound dataset spanning 5 organs and 8 segmentation tasks. Extensive experiments demonstrate that ProPL outperforms state-of-the-art methods across various metrics, establishing a new benchmark for universal ultrasound image segmentation.
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