Keywords: Analysis/Processing, Segmentation
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.
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