# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 4 Experiments  
### Dataset  
In this study, we utilized a tooth dataset provided by Hangzhou Dental Hospital for training, testing, and evaluation. Before the utilization of the dataset, informed consent was obtained from the hospital, granting permission to use their three-dimensional CBCT images for research purposes. Additionally, to ensure the privacy and anonymity of the patients, their data was thoroughly anonymized during the preprocessing phase. The dataset consists of 46 samples, each being a three-dimensional CBCT image with a shape size of (D \(\times\) 536 \(\times\) 536). Here, D represents the depth, ranging from 186 to 359. Each sample has  
Figure 4: The Process of Generative Adversarial Training. Through continuous alternating training of the discriminator and generator, the objective is to enhance the quality of generated labels from the generator and improve the precision of the discriminator’s judgments.  
been carefully annotated, providing detailed descriptions of the three-dimensional model of the teeth. In medical small-sample segmentation tasks, experimenting with varying quantities and sizes of training samples contributes to a comprehensive evaluation of model performance. This experimental design can reveal the model's robustness to changes in data volume and its performance across diverse scenarios. By incrementally increasing the number of training samples, we can observe variations in the model's performance under different sample conditions. To thoroughly validate the segmentation performance of PPA-GAN under small-sample scenarios, we conducted four sets of experiments based on different training sample sizes. In these four experiments, we randomly selected 5, 10, 15, and 20 samples as the training set, using the remaining samples as the test set. In each experiment set, training and testing were performed for each network, ensuring consistency in the training and test sets among different networks within the same experiment set to minimize the impact of other factors on the experiment results. Our goal is to gain insights into the segmentation effectiveness of PPA-GAN when dealing with a restricted training dataset and to compare its performance with current state-of-the-art segmentation models. These advanced models include 3 DUnet, Swin Unetr, SAM, TransBTS, and the original VNet.  
To ensure the smooth operation of the network and enhance its performance, we performed a series of operations on the data: (1) Prior to experimentation, we scaled the values of CBCT images to the range of 0-255, improving data stability and avoiding issues like gradient explosion or vanishing gradients. (2) We transformed the image shape to (256 \(\times\) 256 \(\times\) 256), reducing the computational burden on the network and adapting to the input requirements of the dual encoder. (3) Before inputting the images into the network, we performed data augmentation by flipping the images along the X and Y axes with a 15% probability. This increased the model's generalization capability, enabling it to better adapt to previously unseen data, while also enhancing the diversity of the dataset.