U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCT

• URL: http://arxiv.org/abs/2509.20154v2
• Date: Tue, 30 Sep 2025 12:23:13 GMT
• Title: U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCT
• Authors: Zhi Qin Tan, Xiatian Zhu, Owen Addison, Yunpeng Li,
• Abstract summary: We propose U-Mamba2-SSL, a novel semi-supervised learning framework that builds on the U-Mamba2 model and employs a multi-stage training strategy.<n>U-Mamba2-SSL achieved an average score of 0.789 and a DSC of 0.917 on the hidden test set, achieving first place in Task 1 of the STSR 2025 challenge.
• Score: 44.3806898357896
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate segmentation of teeth and pulp in Cone-Beam Computed Tomography (CBCT) is vital for clinical applications like treatment planning and diagnosis. However, this process requires extensive expertise and is exceptionally time-consuming, highlighting the critical need for automated algorithms that can effectively utilize unlabeled data. In this paper, we propose U-Mamba2-SSL, a novel semi-supervised learning framework that builds on the U-Mamba2 model and employs a multi-stage training strategy. The framework first pre-trains U-Mamba2 in a self-supervised manner using a disruptive autoencoder. It then leverages unlabeled data through consistency regularization, where we introduce input and feature perturbations to ensure stable model outputs. Finally, a pseudo-labeling strategy is implemented with a reduced loss weighting to minimize the impact of potential errors. U-Mamba2-SSL achieved an average score of 0.789 and a DSC of 0.917 on the hidden test set, achieving first place in Task 1 of the STSR 2025 challenge. The code is available at https://github.com/zhiqin1998/UMamba2.

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