Airway Segmentation

Airway segmentation, the automated identification and delineation of airways in medical images like CT scans, aims to improve the efficiency and accuracy of diagnosing and monitoring lung diseases. Current research focuses on enhancing segmentation accuracy and completeness, particularly in challenging cases with complex pathologies or limited data, employing various deep learning architectures such as U-Net and its variants, transformer networks, and graph neural networks, often incorporating techniques like active learning and uncertainty estimation to optimize annotation efforts. These advancements have significant implications for clinical practice, enabling faster and more objective assessment of lung disease severity and facilitating improved treatment planning for procedures like bronchoscopy.