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Abstract #1717

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

Keying Qi1,2, Chenchen Yan3, Donghao Niu1,2, Bing Zhang 3, Dong Liang1,4, and Xiaojing Long1,4
1Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, Shenzhen, China, 2Department of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China., Beijing, China, 3Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China, Nanjing, China, 4The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China

Synopsis

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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