Abstract: Segmenting pulmonary airways from computed tomography (CT) images holds significant importance for the diagnosis and treatment of lung diseases. In recent years, deep learning-based methods for pulmonary airway segmentation have made considerable progress. However, achieving high- precision segmentation still poses substantial challenges. The class imbalance present in pulmonary airway CT images severely affects segmentation performance. To address this issue, a novel segmentation method employing an adaptive combined loss function is proposed. Firstly, by utilizing a local imbalance strategy, the model’s ability to discriminate foreground voxels is enhanced. Secondly, radial distance information is integrated into the focal loss function to improve the model’s capacity to automatically identify small airways. Finally, topological continuity of the airways is enhanced based on topology sensitivity and topology precision. Experimental results demonstrate that, compared to the state-of-the-art models, the proposed method’s model achieves the best performance in terms of the dice similarity coefficient (DSC), branch detection rate(BD), and tree length detection rate(TLD), thereby improving the performance of pulmonary airway segmentation.

Key words: pulmonary airway segmentation, deep learning, class imbalance, airway continuity, adaptive combined loss function