Context-Driven Active Contour (CDAC): A Novel Medical Image Segmentation Method Based on Active Contour and...

Lung diseases, including chronic obstructive pulmonary disease (COPD) and pulmonary fibrosis, pose significant health challenges due to their high morbidity and mortality rates. Computed tomography (CT) scans play a critical role in early diagnosis and disease management, yet traditional segmentation methods often falter in addressing anatomical variability and pathological complexity. To overcome these limitations, this study introduces the context-driven active contour (CDAC), a new segmentation method that combines active contour models (ACMs) with contextual analysis. CDAC leverages contextual information from image embeddings and expert annotations to refine segmentation precision. The algorithm employs contextual attention force (CAF) as an external energy term and contextual balloon force (CBF) as an internal energy term, enabling robust contour adaptation. Evaluations were conducted on CT images of healthy lungs, as well as those affected by COPD and pulmonary fibrosis. CDAC achieved notable performance metrics, including a Dice coefficient of 96.8% for healthy lungs, an Accuracy of 94.5% for COPD, and a Jaccard Index of 92.3% for pulmonary fibrosis. These results demonstrate the method's effectiveness and adaptability. By integrating contextual insights, CDAC offers a promising solution for enhancing computer-aided diagnostic (CAD) systems in the management of lung diseases.