Cone-Beam Computed Tomography (CBCT) is a widely used 3D imaging technique in dentistry, providing volumetric information about the anatomical structures of jaws and teeth. Accurate segmentation of these anatomies is critical for clinical applications such as diagnosis and surgical planning, but remains time-consuming and challenging. In this paper, we present U-Mamba2, a new neural network architecture designed for multi-anatomy CBCT segmentation in the context of the ToothFairy3 challenge. U-Mamba2 integrates the Mamba2 state space models into the U-Net architecture, enforcing stronger structural constraints for higher efficiency without compromising performance. In addition, we integrate interactive click prompts with cross-attention blocks, pre-train U-Mamba2 using self-supervised learning, and incorporate dental domain knowledge into the model design to address key challenges of dental anatomy segmentation in CBCT. Extensive experiments, including independent tests, demonstrate that U-Mamba2 is both effective and efficient, securing first place in both tasks of the Toothfairy3 challenge. In Task 1, U-Mamba2 achieved a mean Dice of 0.84, HD95 of 38.17 with the held-out test data, with an average inference time of 40.58s. In Task 2, U-Mamba2 achieved the mean Dice of 0.87 and HD95 of 2.15 with the held-out test data. The code is publicly available at https://github.com/zhiqin1998/UMamba2.

翻译：锥形束计算机断层扫描（CBCT）是牙科领域广泛使用的三维成像技术，能够提供颌骨与牙齿解剖结构的体数据信息。对这些解剖结构进行精确分割对于诊断与手术规划等临床应用至关重要，但该任务仍耗时且具有挑战性。本文提出U-Mamba2，一种专为ToothFairy3挑战赛中的多解剖结构CBCT分割设计的新型神经网络架构。U-Mamba2将Mamba2状态空间模型集成到U-Net架构中，通过强化结构约束在保持性能的同时实现更高效率。此外，我们通过交叉注意力模块整合交互式点击提示，采用自监督学习对U-Mamba2进行预训练，并将牙科领域知识融入模型设计，以应对CBCT牙齿解剖分割中的关键挑战。大量实验（包括独立测试）表明，U-Mamba2兼具高效性与有效性，在Toothfairy3挑战赛两项任务中均位列第一。在任务1中，U-Mamba2在保留测试数据上取得平均Dice系数0.84、HD95值38.17，平均推理时间为40.58秒；在任务2中，模型在保留测试数据上获得平均Dice系数0.87、HD95值2.15。代码已公开于https://github.com/zhiqin1998/UMamba2。