用双学生教师框架融合SAM，提升产科超声中耻骨联合与胎头分割精度
DSTCS: Dual-Student Teacher Framework with Segment Anything Model for Semi-Supervised Pubic Symphysis Fetal Head Segmentation
- 设计双学生-教师架构，协同CNN与SAM分支进行联合学习
- 在MICCAI 2023/2024数据集上达到新高，边界分割更准确
- 适合医学图像分割研究者及产科影像临床应用开发者
耻骨联合与胎头（PSFH）的分割是产程监测中的关键步骤，对评估分娩进展和识别潜在并发症至关重要。然而，由于类别不平衡、边界模糊及超声图像噪声干扰，加之高质量标注数据稀缺，准确分割仍面临挑战。现有研究主要依赖CNN与Transformer架构，未充分挖掘更强大模型的潜力。本文提出一种结合CNN与段一切换模型（SAM）的双学生-教师框架（DSTCS），通过协同学习机制显著提升分割精度。该方案还引入针对边界优化的数据增强策略与新型损失函数。在MICCAI 2023和2024 PSFH分割基准上的大量实验表明，本方法具备更强鲁棒性，显著优于现有技术，为临床实践提供了可靠的分割工具。
原文摘要 · Abstract (English)
Segmentation of the pubic symphysis and fetal head (PSFH) is a critical procedure in intrapartum monitoring and is essential for evaluating labor progression and identifying potential delivery complications. However, achieving accurate segmentation remains a significant challenge due to class imbalance, ambiguous boundaries, and noise interference in ultrasound images, compounded by the scarcity of high-quality annotated data. Current research on PSFH segmentation predominantly relies on CNN and Transformer architectures, leaving the potential of more powerful models underexplored. In this work, we propose a Dual-Student and Teacher framework combining CNN and SAM (DSTCS), which integrates the Segment Anything Model (SAM) into a dual student-teacher architecture. A cooperative learning mechanism between the CNN and SAM branches significantly improves segmentation accuracy. The proposed scheme also incorporates a specialized data augmentation strategy optimized for boundary processing and a novel loss function. Extensive experiments on the MICCAI 2023 and 2024 PSFH segmentation benchmarks demonstrate that our method exhibits superior robustness and significantly outperforms existing techniques, providing a reliable segmentation tool for clinical practice.
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