# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 2 Related Work  
Our work is most closely aligned with research in the field of pneumothorax segmentation methodology. Numerous studies have investigated approaches for detecting and segmenting pneumothorax in chest radiographs using AI and deep learning techniques. For instance, [PERSON] et al. introduced CheXLocNet, a deep convolutional neural network-based framework designed to automatically localize pneumothorax in chest radiographs, highlighting the potential of deep learning for both localization and detection tasks [12]. In a separate contribution, [PERSON] et al. also proposed a two-stage deep learning model that integrates segmentation and classification components to enhance the accuracy of pneumothorax segmentation [13]. These studies underscore the growing interest in leveraging deep learning to improve the reliability and automation of pneumothorax diagnosis. In addition to these methods, [PERSON] et al. investigated the factors that enhance the positive predictive value of AI-based pneumothorax detection, showing that incorporating clinical context and improving AI models' sensitivity can lead to better performance in chest radiographs [14]. [PERSON] et al. utilized deep learning algorithms to automate pneumothorax triaging in chest X-rays, focusing on the New Zealand population, and demonstrated the potential of AI for large-scale medical screening [15] and diagnosis. [PERSON] et al. implemented deep learning models for pneumothorax detection specifically after needle biopsy, which highlights the clinical relevance and real-world implementation of AI in medical practices [16]. Moreover, [PERSON] et al. applied a deep multi-instance transfer learning approach for pneumothorax classification, emphasizing the importance of transfer learning in enhancing model generalization across different data distributions [17]. [PERSON] et al. explored novel techniques like the exponential pixelating integral transform for enhanced chest X-ray abnormality detection, which could be useful for improving the detection of pneumothorax [18]. Additionally, [PERSON] et al. proposed an automated pneumothorax segmentation and quantification algorithm based on deep learning, improving the precision of both segmentation and measurement tasks in X-ray images [19]. [PERSON] et al. focused on leveraging anatomical constraints with uncertainty modeling for pneumothorax segmentation, aiming to improve segmentation accuracy by incorporating prior anatomical knowledge into the learning process [20]. While the most common imaging modality for segmentation and quantification of pneumothorax is chest X-ray, computed tomography (CT) is also used for more reliable three-dimensional imaging. Several studies have discussed how deep learning can enable high-performance automatic segmentation of pneumothorax from CT scans [21; 22; 23]. These studies demonstrate a wide range of approaches, from deep learning-based localization and segmentation to multi-instance learning and transfer learning techniques, showing significant progress in automating pneumothorax detection and quantification from chest radiographs. Despite the significant advancements presented in these studies, several limitations remain. Many of the existing approaches are tailored to specific datasets or clinical settings, limiting their generalizability across diverse patient populations and imaging conditions. Additionally, while several models achieve high performance in detection or classification, fewer works focus on fine-grained segmentation with spatial accuracy, particularly in complex cases involving overlapping anatomical structures or subtle pneumothorax presentations. Most models also lack the integration of anatomical priors or contrast enhancement mechanisms, which are crucial for robust segmentation in low-contrast X-ray images. Furthermore, limited attention has been given to model interpretability and the incorporation of interactive tools that enable radiologists to refine automated results in real time.