DSTCS: Dual-Student Teacher Framework with Segment Anything Model for Semi-Supervised Pubic Symphysis Fetal Head Segmentation

• URL: http://arxiv.org/abs/2601.19446v1
• Date: Tue, 27 Jan 2026 10:32:28 GMT
• Title: DSTCS: Dual-Student Teacher Framework with Segment Anything Model for Semi-Supervised Pubic Symphysis Fetal Head Segmentation
• Authors: Yalin Luo, Shun Long, Huijin Wang, Jieyun Bai,
• Abstract summary: We propose a framework combining CNN and SAM, which integrates the Segment Anything Model (SAM) into a dual student-teacher architecture.<n>A cooperative learning mechanism between the CNN and SAM branches significantly improves segmentation accuracy.<n>Our method exhibits superior robustness and significantly outperforms existing techniques, providing a reliable segmentation tool for clinical practice.
• Score: 3.576367560614242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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