3D Visualization of Pulmonary Vessel Based on Low-Cost Segmentation and Fast Reconstruction.

Real-time visual-aided navigation and path strategy for pneumonoconiosis and efficient 3D visualization of pulmonary vessels are of great research and clinical significance in the treatment of lung diseases. The complex structure of lung tissue limits the application of deep learning in pulmonary vascular visualization due to the lack of vascular labeling datasets. Also, the existing methods have large computational complexity and are low efficiency. This study proposes a method for high-quality 3D visualization of pulmonary vessels based on low-cost segmentation and fast reconstruction, consisting of three steps: 1) Pulmonary vessel feature extraction from lung CT images using self-supervised learning, 2) Segmentation of pulmonary sparse vessels in lung CT images using self-supervised transfer learning, and 3) 3D reconstruction of pulmonary vessels based on segmentation results of step (2) using interpolation. The accuracy of pulmonary vascular contour segmentation was improved from 91.31% using the sparse coding to 98.65% using our proposed method (27,270 test sample points); the classifier evaluation accuracy was improved from 95.33% to 98.26%, and the average running time of the model with the test set data was 44 ms per slice. the segmentation results can automatically generate a complete vascular tree model with an average time of 10.8s +/- 1 1.6s. The results demonstrate that the proposed method provides fast and accurate 3D visualization of pulmonary vessels, and is promising for more precise and reliable information for pneumonoconiosis patients.