\section{Conclusion}

We presented U-Mamba2-SSL, a novel multi-stage semi-supervised learning framework for tooth and pulp segmentation in CBCT scans, in the scope of the STSR 2025 challenge. The framework consists of first pre-training U-Mamba2 with the disruptive autoencoder, utilizing unlabeled data for consistency regularization, and a pseudo labeling strategy in the final stage.
Our results demonstrate that the proposed framework can substantially enhance model performance, achieving first place with an average score of 0.789 on the hidden test set in Task 1 of the STSR 2025 challenge.

\subsubsection{Acknowledgements} We thank all the data owners for making the medical images publicly available and Codabench~\cite{codabench} for hosting the challenge platform.

\subsubsection{Disclosure of Interests.} The authors have no competing interests to declare that are relevant to the content of this article.
