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.

翻译：耻骨联合与胎头（PSFH）分割是产时监护中的关键步骤，对于评估产程进展及识别潜在分娩并发症至关重要。然而，由于超声图像中存在类别不平衡、边界模糊及噪声干扰等问题，加之高质量标注数据稀缺，实现精确分割仍面临重大挑战。当前PSFH分割研究主要依赖于CNN与Transformer架构，更强大模型的潜力尚未得到充分探索。本研究提出一种结合CNN与SAM的双学生-教师框架（DSTCS），将Segment Anything Model（SAM）集成到双学生-教师架构中。CNN与SAM分支间的协同学习机制显著提升了分割精度。该方案还引入了针对边界处理优化的专用数据增强策略及新型损失函数。在MICCAI 2023与2024 PSFH分割基准上的大量实验表明，本方法展现出卓越的鲁棒性，显著优于现有技术，为临床实践提供了可靠的分割工具。