Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
This research provides improved segmentation of pulmonary airways and vessels for medical image analysis, particularly benefiting clinicians and researchers studying lung diseases by enabling more detailed anatomical insights.
This paper addresses the challenge of segmenting pulmonary airways, arteries, and veins in CT scans, which is difficult due to class imbalance. The authors developed a CNN-based method that significantly improves sensitivity to peripheral bronchioles, arterioles, and venules, extracting more branches than state-of-the-art methods while maintaining competitive overall segmentation performance.
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://www.pami.sjtu.edu.cn/News/56