\section{Conclusion}
We presented a new architecture, U-Mamba2, designed for multi-anatomy segmentation of CBCT images in the scope of the ToothFairy3 challenge. U-Mamba2 integrates the Mamba2 SSD framework into the U-Net backbone, achieving higher efficiency without compromising performance compared to U-Mamba. By incorporating domain-specific knowledge of dental anatomy, we improved the model's performance on multi-anatomy segmentation of CBCT scans. Both the validation and independent test results demonstrate the effectiveness and efficiency of U-Mamba2, securing first place in both Tasks 1 and 2 of the ToothFairy3 challenge. 
