# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 6 Discussion  
The experimental results demonstrate that the PPA-SAM model exhibits outstanding segmentation performance and generalization ability on a small-sample CBCT dental dataset, showcasing significant clinical application value. The generator of PPA-SAM combines an optimizer-equipped SAM encoder with a VNet encoder, forming a dual-encoder structure that facilitates the acquisition of both global and local information. In dental images, global information focuses on overall structure and positioning, while local information involves the details and edges of each tooth. This combination enables the model to more accurately segment each tooth. The features from the dual-encoder entering the decoder provide it with more information, covering features at different scales and levels. This allows the decoder to better accomplish the three-dimensional tooth segmentation task using multi-level, rich features. We utilize a VNet network with a dual-encoder as the generator, combined with a discriminator containing a three-layer convolutional network, forming the conditional generative adversarial network known as PPA-SAM. Simultaneously, CBCT images are  
Figure 8: Qualitative Visualization Comparison of PPA-SAM and other networks on the 40 th sample in the small-sample tooth segmentation task. Figure 7: Qualitative Visualization Comparison of PPA-SAM and other networks on the 32 nd sample in the small-sample tooth segmentation task. integrated as conditions with labels and input into the discriminator. This conditional information helps enhance the model's generalization ability, enabling it to comprehensively and representatively learn features and better understand the relationship between teeth and their surrounding environment, resulting in segmented three-dimensional tooth images that closely resemble real data. In the tooth segmentation task of this study, PPA-SAM demonstrates higher Dice coefficients compared to other comparative networks across different scales of training datasets. Specifically, for training set sizes of 5, 10, 15, and 20, the PPA-SAM shows an improvement in Dice coefficients over the second-ranking model by 1.85%, 2.41%, 3.08%, and 0.87%, respectively. These results indicate that even with smaller amounts of data, the PPA-SAM can achieve superior segmentation performance, highlighting its significant potential for clinical applications. Traditional convolutional networks [6; 7; 8; 9; 10; 31; 32; 33] typically require large-scale datasets and perform poorly in situations with small samples, especially in medical segmentation tasks where datasets are often lim-ited. For example, 3D Unet [33] has long training times and faces significant computa-tional pressure. Transformer-based segmentation networks, such as Swin Unet [32], perform poorly and have limited generalization capability when trained with limited data. Visualizing the three-dimensional segmentation results from Figures 5-7, irregular fragmented regions are observed in the segmented 3D models generated by Swin Unet, while TransBTS exhibits contiguous phenomena in the root regions of the teeth, with a lower accuracy in detail. This suggests potential overfitting issues of Swin Unet and TransBTS in small-sample segmentation tasks, leading to decreased seg-mentation accuracy. However, due to the two-dimensional shape of input and output data for the SAM, the inter-slice correlation in the SAM's three-dimensional segmenta-tion results is relatively low. When segmenting individual slices, it is prone to misiden-tifying surrounding tissues as teeth, resulting in fine fragments around the tooth mod-el, and the weak correlation between slices leads to a staircase-like appearance on the surface of the 3D segmentation result. In comparison, 3D Unet and VNet, as traditional segmentation models, demonstrate good segmentation performance. However, through three-dimensional and two-dimensional visualizations, it can be observed that the segmentation accuracy in detail areas such as between the roots and teeth remains low.