\begin{abstract}



Accurate segmentation of teeth and pulp in Cone-Beam Computed Tomography (CBCT) is vital for clinical applications like treatment planning and diagnosis. However, manual segmentation requires extensive expertise and is exceptionally time-consuming, highlighting the critical need for automated semi-supervised segmentation algorithms that can utilize unlabeled data.
In this paper, we propose {\bf 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 obtained 0.917 DSC and 0.948 mIoU on the hidden test set, achieving first place in Task 1 of the STSR 2025 challenge.
The code is available at \url{https://github.com/zhiqin1998/UMamba2}.



\keywords{Semi-supervised learning \and U-Mamba2-SSL \and CBCT Imaging \and Tooth and Pulp Segmentation \and STSR 2025 Challenge}

\end{abstract}
