Pulmonary Segment

Pulmonary segment analysis focuses on precisely identifying and delineating the individual segments of the lung, crucial for tasks like surgical planning and disease assessment. Current research emphasizes automated segmentation using deep learning approaches, including implicit surface models and ensemble learning methods based on convolutional neural networks, to improve accuracy and efficiency compared to traditional techniques. These advancements are significantly impacting medical imaging analysis, particularly in diagnosing and managing lung diseases like COVID-19, by enabling quantitative assessment of lung pathology and facilitating more precise treatment strategies. The development of robust and reliable automated segmentation methods holds considerable promise for improving patient care and clinical decision-making.