Lung Region

Lung region analysis focuses on accurately segmenting and quantifying lung structures and pathologies from medical images like CT scans and X-rays, aiding in diagnosis and disease monitoring. Current research emphasizes developing robust deep learning models, including UNets, DenseNets, and diffusion models, to address challenges such as class imbalance, scanner variability, and subjective annotation, often incorporating techniques like attention mechanisms and contrastive learning for improved performance. These advancements are crucial for improving the accuracy and efficiency of lung disease diagnosis, particularly for conditions like COPD and pulmonary hypertension, enabling more precise quantification of disease severity and facilitating better patient management.