# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 4 Results
### Comparison with Other Datasets  
We further apply our model to the public SIIM-ACR pneumothorax segmentation dataset to evaluate its generalization ability [33]. SIIM-ACR is a dataset designed for the SIIM-ACR pneumothorax segmentation challenge, supporting both segmentation and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fold** & **Model** & **Dice** & **Accuracy** & **Precision** & **Recall** & **IoU** \\ \hline \multirow{2}{*}{1} & nnUNet & 0.6723 & 0.9939 & 0.8156 & 0.6165 & 0.5363 \\  & nnUNet + SPCA & 0.7991 & 0.9933 & 0.8482 & 0.7766 & 0.6768 \\ \hline \multirow{2}{*}{2} & nnUNet & 0.6312 & 0.9921 & 0.7491 & 0.6570 & 0.4991 \\  & nnUNet + SPCA & 0.8274 & 0.9927 & 0.8498 & 0.8251 & 0.7155 \\ \hline \multirow{2}{*}{3} & nnUNet & 0.6757 & 0.9956 & 0.7281 & 0.6979 & 0.5283 \\  & nnUNet + SPCA & 0.8127 & 0.9953 & 0.8481 & 0.8056 & 0.6989 \\ \hline \multirow{2}{*}{4} & nnUNet & 0.5490 & 0.9938 & 0.7948 & 0.5464 & 0.4072 \\  & nnUNet + SPCA & 0.8305 & 0.9935 & 0.8610 & 0.8320 & 0.7234 \\ \hline \multirow{2}{*}{5} & nnUNet & 0.6668 & 0.9945 & 0.7557 & 0.6742 & 0.5196 \\  & nnUNet + SPCA & 0.8159 & 0.9950 & 0.8757 & 0.7888 & 0.7016 \\ \hline \multirow{2}{*}{Average} & nnUNet & 0.639 & **0.9940** & 0.7687 & 0.6384 & 0.4981 \\  & nnUNet + SPCA & **0.8171** & **0.9940** & **0.8566** & **0.8056** & **0.7032** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of segmentation accuracy comparing standard nnUNet and SPCA-enhanced nnUNet on pneumothorax segmentation in chest radiography. Bold indicates the top values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fold** & **Model** & **Dice** & **IoU** \\ \hline \multirow{3}{*}{1} & SPCA-enhanced nnUNet & 0.7991 & 0.6768 \\  & Without CBAM module & 0.6969 & 0.5848 \\  & Without SPCA module & 0.8066 & 0.6824 \\ \hline \multirow{3}{*}{2} & SPCA-enhanced nnUNet & 0.8274 & 0.7155 \\  & Without CBAM module & 0.6335 & 0.5248 \\  & Without SPCA module & 0.8171 & 0.7119 \\ \hline \multirow{3}{*}{3} & SPCA-enhanced nnUNet & 0.8127 & 0.6989 \\  & Without CBAM module & 0.5655 & 0.4659 \\  & Without SPCA module & 0.7169 & 0.6129 \\ \hline \multirow{3}{*}{4} & SPCA-enhanced nnUNet & **0.8305** & **0.7234** \\  & Without CBAM module & 0.6865 & 0.5849 \\  & Without SPCA module & 0.8064 & 0.6975 \\ \hline \multirow{3}{*}{5} & SPCA-enhanced nnUNet & 0.8159 & 0.7016 \\  & Without CBAM module & 0.6602 & 0.5417 \\  & Without SPCA module & 0.7818 & 0.6753 \\ \hline \multirow{3}{*}{Average} & SPCA-enhanced nnUNet & **0.8171** & **0.7032** \\  & Without CBAM module & 0.6487 & 0.5415 \\ \cline{1-1}  & Without SPCA module & 0.7858 & 0.6760 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study on SPCA-enhanced nnUNet. An ablation study was implemented on the SPCA module and the CBAM module. Bold indicates the top values.  
classification tasks. It consists of 12,047 images with 2669 labeled pneumothorax objects. For this comparative study, we used 300 subjects from the SIIM-ACR dataset, allocating 216 for training, 54 for validation, and 30 for testing, using 5-fold cross-validation, with 1000 epochs per fold. Results are shown in Figure 9 and Table 3.