# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 6 Conclusions  
In this study, we presented SPCA-enhanced nnUNet, an improved version of the nnUNet architecture specifically designed for pneumothorax segmentation in chest X-ray radiography. Pneumothorax, with its varied presentation and subtle features in radiographs, poses a challenge for accurate and efficient detection. The modifications made to the nnUNet, including enhanced data augmentation strategies, optimized preprocessing techniques, and architectural refinements, have been shown to significantly improve segmentation performance in terms of both accuracy and robustness. At the same time, the ablation study confirms that integrating attention mechanisms into the model leads to notable performance improvements. By enabling the model to focus on the most informative regions of the image, attention enhances feature representation and facilitates more accurate image comparison. This highlights the crucial role of attention in improving both the precision and robustness of image-processing tasks. Through extensive experiments on a publicly available pneumothorax dataset, our proposed model demonstrated superior performance over baseline methods, achieving notable improvements in the Dice score and other key evaluation metrics. Additionally, the model's ability to generalize well across diverse datasets underlines its potential for deployment in clinical practice, where the variability of X-ray radiography is a significant concern. These results underscore the efficacy of the SPCA-enhanced nnUNet as a reliable tool for the automated segmentation of pneumothorax in chest X-rays. The model holds promise for assisting radiologists in the early detection of pneumothorax, potentially reducing diagnostic time and improving patient outcomes. However, further optimization is still required to adapt the model to diverse clinical environments. Future work will focus on fine-tuning the model for broader applications and exploring its integration into real-world diagnostic systems. By enhancing diagnosis accuracy and accelerating image interpretation, AI is playing an increasingly pivotal role in medical imaging. In the diagnosis of pneumothorax, an acute condition requiring rapid intervention, deep convolutional neural networks have demonstrated robust performance in detecting subtle radiographic indicators. This information can assist physicians in estimating pneumothorax volume and monitoring disease progression. However, despite these advances, AI remains a supportive tool rather than a replacement for human expertise [40]. The clinical interpretation of imaging findings still requires nuanced judgment. AI systems should, therefore, be viewed as augmentative technologies that empower radiologists and clinicians by reducing cognitive load, minimizing human error, and enabling more consistent and efficient diagnostic workflows. **Author Contributions:** Conceptualization, supervision, funding acquisition, writing--original draft, and project administration: E.A.R. ; software, validation, formal analysis, investigation, visualization, and data curation: Y.J. ; methodology and writing--review and editing: Y.J. and E.A.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by JST, PRESTO Grant Number JPMJPR23P7, Japan. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used in this study is available from [[https://figshare.com/articles/dataset/CANDID-PTX/14173982](https://figshare.com/articles/dataset/CANDID-PTX/14173982)]([https://figshare.com/articles/dataset/CANDID-PTX/14173982](https://figshare.com/articles/dataset/CANDID-PTX/14173982)) (accessed on 11 June 2025). A data use agreement and completion of an ethical training course may be required to access the data. **Acknowledgments:** The authors would like to thank [PERSON] from the Department of Radiology, Dunedin Hospital, Dunedin, New Zealand, for providing the CANDID-PTX dataset.