The segmentation of the pubic symphysis and fetal head (PSFH) constitutes a pivotal step in monitoring labor progression and identifying potential delivery complications. Despite the advances in deep learning, the lack of annotated medical images hinders the training of segmentation. Traditional semi-supervised learning approaches primarily utilize a unified network model based on Convolutional Neural Networks (CNNs) and apply consistency regularization to mitigate the reliance on extensive annotated data. However, these methods often fall short in capturing the discriminative features of unlabeled data and in delineating the long-range dependencies inherent in the ambiguous boundaries of PSFH within ultrasound images. To address these limitations, we introduce a novel framework, the Dual-Student and Teacher Combining CNN and Transformer (DSTCT), which synergistically integrates the capabilities of CNNs and Transformers. Our framework comprises a Vision Transformer (ViT) as the teacher and two student mod ls one ViT and one CNN. This dual-student setup enables mutual supervision through the generation of both hard and soft pseudo-labels, with the consistency in their predictions being refined by minimizing the classifier determinacy discrepancy. The teacher model further reinforces learning within this architecture through the imposition of consistency regularization constraints. To augment the generalization abilities of our approach, we employ a blend of data and model perturbation techniques. Comprehensive evaluations on the benchmark dataset of the PSFH Segmentation Grand Challenge at MICCAI 2023 demonstrate our DSTCT framework outperformed ten contemporary semi-supervised segmentation methods. Code available at https://github.com/jjm1589/DSTCT.

本研究解决了分割聚合骶骨和胎头（PSFH）在监测分娩进程及识别潜在并发症方面的不足，尤其是在缺乏标注医学图像情况下的分割训练问题。我们提出了一种新颖的双学生和教师结合CNN与Transformer的框架，通过互相监督的方式生成硬标和软标，提高了分割精度。实验结果表明，该框架超越了十种现有的半监督分割方法，具有显著的推广能力和应用前景。

基于双学生-教师框架的聚合骶骨和胎头超声图像分割