# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 5 Result  
To comprehensively validate the segmentation performance of PPA-SAM, we chose VNet as a baseline and assessed the impact of introducing the enhanced SAM encoder and generative adversarial network on segmentation performance. Simultaneously, we conducted performance comparisons with commonly used models. Table 1 presents the segmentation performance of multiple models on the tooth dataset with varying numbers of training samples (5, 10, 15, 20). By incrementally increasing the number of training samples, we evaluated each model's performance under small-sample conditions. The results indicate that the introduction of the enhanced SAM encoder and generative adversarial network indeed enhances segmentation performance. In the tooth segmentation task with a training sample size of 20, PPA-SAM exhibited outstanding performance, achieving a Dice coefficient of 94.63, a 95 Hausdorff distance of 0.845, ASD of 0.207, and Jaccard coefficient of 9018. In comparison to other advanced models, PPA-SAM demonstrated superior tooth segmentation results under small-sample training conditions. Conversely, Swin Unetr showed inferior performance in small-sample tasks, with metrics consistently lower than those of 3 DUnet. For the SAM model, being a 2D segmentation model, we extracted individ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Sample Size** & **Dice** & **HD95** & **ASD** & **Jaccard** \\ \hline \multirow{4}{*}{3 DUnet} & 5 & 8871 & 2.126 & 0.521 & 8209 \\  & 10 & 8954 & 1.576 & 0.358 & 8513 \\  & 15 & 9075 & 1.047 & 0.245 & 8307 \\  & 20 & 9299 & 0.961 & 0.234 & 8711 \\ \hline \multirow{4}{*}{Swin Unetr} & 5 & 6158 & 27.14 & 3.008 & 4641 \\  & 10 & 8048 & 3.013 & 0.739 & 6840 \\  & 15 & 8847 & 2.885 & 0.452 & 8006 \\  & 20 & 9182 & 0.977 & 0.251 & 8560 \\ \hline \multirow{4}{*}{VNet} & 5 & 8346 & 1.615 & 0.426 & 7203 \\  & 10 & 8884 & 1.163 & 0.298 & 8034 \\  & 15 & 9063 & 0.997 & 0.253 & 8336 \\  & 20 & 9376 & 0.920 & 0.312 & 8156 \\ \hline \multirow{4}{*}{TransBTS} & 5 & 8166 & 3.934 & 0.740 & 6923 \\  & 10 & 8470 & 6.020 & 0.758 & 7379 \\  & 15 & 8938 & 1.047 & 0.281 & 8083 \\  & 20 & 9101 & 0.924 & 0.251 & 8354 \\ \hline \multirow{4}{*}{SAM} & 5 & 7185 & 5.814 & 1.336 & 6441 \\  & 10 & 7250 & 24.56 & 2.895 & 6785 \\  & 15 & 7830 & 2.837 & 0.944 & 7153 \\  & 20 & 8078 & 1.962 & 0.803 & 7341 \\ \hline \multirow{4}{*}{VNet} & 5 & 8562 & 1.436 & 0.595 & 7673 \\  & 10 & 8975 & 1.198 & 0.472 & 8185 \\ \cline{1-1}  & 15 & 9152 & 1.084 & 0.398 & 8424 \\ \cline{1-1}  & 20 & 9352 & 0.981 & 0.347 & 8673 \\ \hline \multirow{4}{*}{VNet} & 5 & 8753 & 1.086 & 0.415 & 7822 \\ \cline{1-1}  & 10 & 8958 & 1.043 & 0.320 & 8154 \\ \cline{1-1}  & 15 & 9177 & 0.982 & 0.273 & 8525 \\ \cline{1-1}  & 20 & 9337 & 0.954 & 0.173 & 8787 \\ \hline \multirow{4}{*}{PPA-SAM} & 5 & 9056 & 1.261 & 0.328 & 8255 \\ \cline{1-1}  & 10 & 9195 & 1.035 & 0.279 & 8627 \\ \cline{1-1}  & 15 & 9383 & 0.945 & 0.216 & 8842 \\ \cline{1-1}  & 20 & 9463 & 0.854 & 0.207 & 9018 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluating the performance of PPA-SAM and other models in the small-sample tooth segmentation task. ual slices from the three-dimensional tooth model, constructing datasets for training and testing. The testing results of the slices were subsequently concatenated and evaluated. Due to SAM's limited pre-training on medical image datasets and its inability to fully leverage three-dimensional information, SAM's performance in segmentation tasks was relatively subpar. We performed three-dimensional visualization on samples numbered 11, 23, and 32, and selected a slice for two-dimensional visualization on sample number 40, as shown in Figures 5-8, revealing that PPA-SAM's segmentation results closely approximated the ground truth labels. In contrast, Swin Unetr exhibited lower accuracy in segmentation with only 5 training samples, making it less suitable for medical-assistive applications.