Accurate segmentation of ultrasound (US) images of the cervical muscles is crucial for precision healthcare. The demand for automatic computer-assisted methods is high. However, the scarcity of labeled data hinders the development of these methods. Advanced semi-supervised learning approaches have displayed promise in overcoming this challenge by utilizing labeled and unlabeled data. This study introduces a novel semi-supervised learning (SSL) framework that integrates dual neural networks. This SSL framework utilizes both networks to generate pseudo-labels and cross-supervise each other at the pixel level. Additionally, a self-supervised contrastive learning strategy is introduced, which employs a pair of deep representations to enhance feature learning capabilities, particularly on unlabeled data. Our framework demonstrates competitive performance in cervical segmentation tasks. Our codes are publicly available onthis https URL\_Cervical\_Segmentation.
View on arXiv@article{wang2025_2503.17057,
title={ Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks },
author={ Fangyijie Wang and Kathleen M. Curran and Guénolé Silvestre },
journal={arXiv preprint arXiv:2503.17057},
year={ 2025 }
}