NHPVS: a nodule-aware deep learning method for high-abundance pulmonary vessel segmentation on chest CT

OBJECTIVE: Deep learning-based segmentation of pulmonary vessels is challenged by vessel-nodule confusion and insufficient detection of small vessels. To address these issues, we propose a Nodule-aware High-abundance Pulmonary Vessel Segmentation (NHPVS) framework that enhances nodule differentiation and improves small vessel segmentation. APPROACH: This multi-center study retrospectively collected 477 chest CT scans comprising non-contrast and contrast scans. A 3D U-Net-based model was trained on 370 scans for pulmonary vessel segmentation. Performance metrics included Dice similarity coefficient (DSC), sensitivity, centerline DSC (cl DSC), 95% Hausdorff distance (HD95), and nodule misclassification rate (NMR). Visual and quantitative assessments were conducted on the external test set. MAIN RESULTS: NHPVS outperformed existing methods, achieving a DSC of 89.2%, a sensitivity of 89.2%, a cl DSC of 93.2%, an HD95 of 1.4 mm, and an NMR of 1.8%. Compared with the state-of-the-art method (nnUNet-v2), NHPVS showed superior segmentation accuracy, vascular continuity, and branch abundance, with improvements of 31.1% in vessel length and 60.5% in branch counts. The volume of small pulmonary vessels (diameter < 3 mm) segmented by NHPVS was significantly greater than that segmented by nnUNet-v2 (p < 0.001). SIGNIFICANCE: By enabling the precise visualization of pulmonary arteries and veins on chest CT, the proposed NHPVS framework addresses the critical need for accurate vascular characterization in disease diagnosis and surgical planning.