Pneumothorax Segmentation

Pneumothorax segmentation, the automated identification of air pockets in the pleural space on medical images, aims to improve diagnostic accuracy and efficiency. Current research focuses on developing and refining deep learning models, including U-Net variations, Vision Transformers, and generative adversarial networks, often incorporating self-supervised learning techniques to address data scarcity and improve robustness. These advancements leverage both image features and, increasingly, contextual information from radiology reports to enhance segmentation accuracy and interpretability, ultimately assisting clinicians in faster and more reliable pneumothorax diagnosis. The integration of anatomical constraints and explainable AI methods further contributes to the reliability and clinical acceptance of these automated systems.