Motivation: Fetal MRI and accurate segmentation are essential for clinical use. Manual segmentation is time-consuming, and deep learning methods require substantial labeled data, which is scarce. Goal(s): This study aims to propose a semi-supervised framework for 3D fetal brain segmentation that achieves satisfactory segmentation results while significantly reducing the amount of labeled data required. Approach: A transformer-based network structure utilizing contrastive learning, mutual learning, and incorporating consistency loss serves as the semi-supervised learning framework. Results: Using eight labeled samples, the proposed method achieved a mean Dice score of 0.81 on the test set, surpassing conventional supervised methods that yielded Dice scores of 0.25-0.35. Impact: This study implemented a semi-supervised learning approach to address the challenges of limited labeled data and high annotation costs in 3D fetal brain segmentation. The performance demonstrates the proposed algorithm's robust potential.
Li et al. (Tue,) studied this question.