# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 5 Discussion
### Discussion  
As shown in Figure 10, SPCA-enhanced nnUNet relies on CANDID-PTX, consisting only of the 2D X-ray radiograph, which limits the ability to analyze and interpret spatial information in three dimensions.  
As we can see from Figure 10, SPCA-enhanced nnUNet still cannot detect pneumothorax highlighted with a red border line. Since 2D images represent individual slices of anatomy, the model cannot leverage the full context provided by the depth of the tissue or organ. This means that pneumothoraxs located in different layers or with overlapping regions in 3D space aren't adequately captured. When one structure partially covers another in a 2D slice, the model struggles to accurately segment the obscured regions, leading to a loss of correct segmentation. This is especially problematic in complex anatomical structures where understanding the relationships between adjacent tissues or organs is crucial. Additionally, the lack of 3D information makes it harder for the model to distinguish between similar tissues or identify boundaries that might be clearer in a 3D volume. This limitation can affect the accuracy of the segmentation, potentially leading to issues in clinical applications where precise delineation of organs or pathologies is essential for diagnosis, treatment planning, and surgical procedures.  
Medical image segmentation models that rely on limited pseudo-labels may still introduce noise, especially in the absence of high-quality manual annotations. This label noise can potentially degrade model performance and affect segmentation reliability in clinical applications. To address this limitation, recent research has proposed noise-robust learning strategies, such as BPT-PLR [39], a balanced partitioning and training framework with pseudo-label relaxed contrastive loss, and progressive sample selection methods based on contrastive learning. Integrating such approaches into the training or fine-tuning stages  
Figure 10: A sample comparison between the segmentation result and the ground truth mask demonstrates the original chest X-ray radiograph (**left**), ground truth mask (**middle**), and segmentation result (**right**). The red boxes show the location of pneumothorax region that is difficult to be detected.  
of medical image segmentation models could further enhance their robustness to noisy or imperfect supervision. This integration may be particularly beneficial in real-world scenarios where obtaining accurate medical image annotations is costly, time-consuming, or inconsistent across annotators. Future work may explore these strategies to improve their performance and generalizability under practical constraints.  
After implementing the model on the new dataset, the model's Dice score dropped by approximately 0.1 compared to previous runs. This decline suggests that the new dataset may be more challenging or less aligned with the training distribution. Despite the drop, the model still shows consistent learning behavior, indicating reasonable generalization. Further improvements may require dataset-specific tuning or augmentation strategies to better adapt to the new data characteristics.