DG-Net：A Semi-Supervised Fetal Brain Segmentation Method Based on Diffusion Model and Guided Consistency

Motivation: Acquiring labeled data for fetal brain tissue segmentation is challenging and costly, limiting traditional supervised learning. Goal(s): This study aims to develop a semi-supervised method to enhance segmentation accuracy by addressing the difficulty in obtaining labeled data. Approach: Employing a single encoder and dual decoder structure, this method integrated a diffusion model to capture invariant features and refined precise features through guided consistent blocks. Results: Experimental outcomes demonstrated that this approach achieved high-precision image segmentation using a limited number of labeled samples, significantly enhancing accuracy while reducing reliance on expert input. Impact: This study introduces a semi-supervised fetal brain tissue segmentation method leveraging the diffusion model and guided consistency. It achieves comparable performance with fewer labeled samples, reducing manual marking time and advancing fetal brain diagnosis.