Abstract
Reconstructing the pulmonary vascular tree from computed tomography (CT) images is essential for quantitative lung analysis, vascular morphology assessment, and patient-specific modeling, yet it remains challenging because vessels span multiple scales, from proximal arteries to distal microvasculature. Clinical chest CT is further affected by limited spatial resolution, partial volume effects, heterogeneous image quality, and respiratory motion artifacts. Unlike deep learning-based pulmonary vessel segmentation methods that require large annotated datasets, we propose a deterministic, training-free, and explainable pipeline for CT-based pulmonary vascular tree reconstruction. The method fuses multiscale Hessian-based Frangi and Sato vesselness filters using a weighted maximum response across 12 spatial scales from 1 to 8 mm, enabling detection of large pulmonary arteries and peripheral branches. Lung parenchyma is segmented by Hounsfield unit thresholding, morphological post-processing, and Chan-Vese active contour refinement. Vascular centerlines are extracted using the Kimimaro implementation of the TEASAR algorithm; separate left- and right-lung vascular graphs are then constructed, pruned, and verified for acyclicity. Geometric plausibility is assessed using volumetric fractal dimension, Strahler order analysis, Horton ratios, and Murray's law. The resulting fractal dimension of approximately 2.3 is consistent with reported values for the human pulmonary vasculature. At the same time, residual deviations in branching metrics reflect distal-vessel truncation caused by finite CT resolution. These results indicate that the proposed explainable pipeline can generate geometrically plausible pulmonary vascular tree models and may support quantitative pulmonary imaging, vascular morphometry, and computational lung modeling.
Abstract (translated)
URL
https://arxiv.org/abs/2607.04457
