Bronchus Segmentation

Bronchus segmentation, the automated identification of airways in medical images like CT scans, aims to improve the accuracy and efficiency of diagnosing and treating respiratory diseases. Current research focuses on addressing challenges such as intensity variations in the images that confuse algorithms, and the difficulty of segmenting small, distal bronchioles. Advanced deep learning models, including U-Net variations and transformer networks, are being developed and refined, often incorporating novel loss functions and training strategies to improve segmentation accuracy and completeness, particularly in the peripheral airways. These advancements hold significant promise for improving the precision of computer-aided diagnosis and potentially guiding minimally invasive procedures like navigation bronchoscopy.