# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 5 Discussion  
### Performance Evaluation  
The comparison between the standard nnUNet and the SPCA-enhanced nnUNet for pneumothorax segmentation in chest radiography reveals notable improvements across all metrics with the enhanced model. Specifically, the SPCA-enhanced nnUNet outperforms the standard model in terms of the Dice coefficient, accuracy, recall, IoU, and precision. The Dice coefficient, which measures the overlap between predicted and ground truth areas, is significantly higher for the enhanced model, indicating better segmentation quality. Similarly, accuracy and recall are improved, reflecting the enhanced model's ability to correctly identify pneumothorax regions while minimizing false negatives. The IoU also sees a marked increase, demonstrating improved spatial consistency between predicted and actual regions. Finally, precision, which indicates the model's ability to avoid false positives, is noticeably better in the SPCA-enhanced version. Overall, the SPCA enhancement boosts the segmentation performance in every aspect, showcasing its effectiveness in pneumothorax segmentation on chest radiographs. Segmentation performance was primarily assessed using the Dice score. As shown in Table 1, SPCA-enhanced nnUNet consistently outperformed standard nnUNet. This \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fold** & **Dice** & **IoU** \\ \hline
1 & 0.6919 & 0.5396 \\ \hline
2 & 0.6897 & 0.5556 \\ \hline
3 & 0.7187 & 0.5676 \\ \hline
4 & 0.7146 & 0.5612 \\ \hline
5 & 0.6806 & 0.5277 \\ \hline Average & 0.6991 & 0.5503 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of segmentation accuracy on the SIIM-ACR dataset. Bold indicates the top values. Figure 9: Performance of the proposed model on the SIIM-ACR dataset. The **left** side demonstrates the training and validation loss while the **right** side demonstrates the Dice score over epochs. improvement can be attributed to several key modifications in the data preprocessing and model structure, such as the contrast enhancement and combination of the attention mechanism with plain nnUNet. These modifications enable the SPCA-enhanced nnUNet to capture finer pneumothorax details in X-ray radiography, leading to higher accuracy in pneumothorax segmentation tasks. In terms of computational efficiency, we measured the training time and inference speed. While the improved nnUNet introduces additional model complexity due to its enhanced architecture, it maintains a reasonable balance between performance and efficiency. The training time of the improved model increased by approximately compared to the original nnUNet, which can be attributed to the increased depth and complexity of the network. However, the inference speed was found to be nearly identical between the two models, demonstrating that the performance gains did not significantly compromise runtime efficiency. The ablation study highlights the effectiveness of the proposed SPCA-enhanced nnUNet in the task of pneumothorax segmentation. Specifically, the full model incorporating both the SPCA and CBAM achieved the highest average Dice coefficient and IoU across five folds. When the CBAM module was ablated, a substantial performance degradation was observed, with the average Dice dropping, indicating the critical role of enhancing feature refinement through attention mechanisms. The exclusion of the SPCA module also led to performance reductions, though to a lesser extent, demonstrating the complementary value of spatial prior information in guiding the precise localization of pneumothorax regions. These findings collectively validate the design of our proposed architecture and underscore the synergistic contributions of SPCA and CBAM in improving segmentation accuracy and robustness. When implementing our model on the SIIM-ACR dataset, it exhibits a decrease of approximately 0.1 in its Dice score. This performance drop may be attributed to differences in data distribution, imaging protocols, or annotation standards across datasets.