Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Semi-supervised learning
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords