Deep learning pulmonary vessel segmentation for contrast-enhanced and non-contrast CT images
Related Experiment Video
Updated: Nov 10, 2026
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
Published on: June 21, 2024
Ying Ming1, Shaoze Luo2, Ruijie Zhao1
1Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Accurate and comprehensive segmentation of pulmonary vasculature is essential for diagnosing various pulmonary diseases and lung surgical planning.
Purpose:
To develop a deep learning model for pulmonary vessels segmentation based on both computed tomography pulmonary angiography (CTPA) and non-contrast computed tomography (NCCT) data, and to evaluate its performance on a clinical dataset.
Methods:
In this retrospective study, we collected 427 cases of chest CT data from different countries and manufacturers, including 213 CTPA cases and 214 NCCT cases. And we designed a CTPA and NCCT ground truth (GT) generation method producing high-accuracy vessel annotation dataset. Then we developed a 3D U-Net based deep learning method with the Vessel Lumen Structure Optimization Module (VLSOM). We numerically evaluated model using metrics including Centerline Recall (Cl-Recall), Centerline DICE (Cl-DICE), Average Symmetric Surface Distance (ASSD) and 95% Hausdorff Distance (HD95) focusing on the integrity of vascular structures. Finally, the model performance was visually assessed in an independent external dataset.
Results:
In the numerical evaluation on the internal test dataset with 57 cases, our model achieved Cl-DICE, Cl-Recall and HD95 values of 0.909 (95% CI [0.904, 0.914]), 0.883 (95% CI [0.873, 0.891]) and 1.861 mm (95% CI [1.674 mm, 2.073 mm]) which is superior to the state of the art (SOTA) baseline model nnU-Net v2 of 0.860 (95% CI [0.852, 0.867]), 0.772 (95% CI [0.760, 0.783])and 2.458 mm (95% CI [2.219 mm, 2.708 mm]) (all p < 0.001). In the clinical visual assessments on the 90 sets of external datasets, the average score for accuracy and robustness, branch abundance, computer-aided detection and vascular continuity 4.52 (95% CI [4.42, 4.62]), 4.82 (95% CI [4.76, 4.90]), 4.58 (95% CI [4.46, 4.69]), and 4.85 (95% CI [4.77, 4.92]) respectively with a full score of 5.
Conclusions:
The proposed pulmonary vessel segmentation algorithm performed well in preserving the connectivity and integrity of the vascular structure on both CTPA and NCCT data with various diseases.
Critical Relevance Statement:
A vessel centerline topology-enhanced deep segmentation model enables accurate and automatic pulmonary vessel segmentation in both computed tomography pulmonary angiography (CTPA) and non-contrast computed tomography (NCCT) images.
