In the past, general medical image models attracted considerable research interest. However, since medical imaging modalities vary widely and often fundamentally differ from RGB images, applying a general segmentation framework to specific tasks usually requires further optimization to achieve satisfactory performance. Cone-beam computed tomography (CBCT) is a commonly used medical imaging technique in dentistry. Optimizing the segmentation process for CBCT images can greatly enhance the effectiveness of computer-aided diagnostic systems in dental applications. In this work, we analyzed the ToothFairy3 dataset and proposed improvements to the nnU-Net framework. While preserving the auto-configuration capabilities of nnU-Net, we introduced targeted optimizations across the data preprocessing pipeline, network architecture, inference process, and postprocessing strategies to enhance performance for the CBCT multi-class segmentation task. Furthermore, the trained multi-class segmentation model can be integrated with user click prompts to train an interactive segmentation model. These modifications collectively reduced inference time, improved model effectiveness, and increased practical applicability. Code is available at https://github.com/kaoquanyu-for/formedseg.git .