High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation
Journal:
arXiv
Published Date:
Mar 21, 2025
Abstract
Accurate segmentation of pulmonary vessels plays a very critical role in
diagnosing and assessing various lung diseases. Currently, many automated
algorithms are primarily targeted at CTPA (Computed Tomography Pulmonary
Angiography) types of data. However, the segmentation precision of these
methods is insufficient, and support for NCCT (Non-Contrast Computed
Tomography) types of data is also a requirement in some clinical scenarios. In
this study, we propose a 3D image segmentation algorithm for automated
pulmonary vessel segmentation from both contrast-enhanced and non-contrast CT
images. In the network, we designed a Vessel Lumen Structure Optimization
Module (VLSOM), which extracts the centerline (Cl) of vessels and adjusts the
weights based on the positional information and adds a Cl-Dice Loss to
supervise the stability of the vessels structure. We used 427 sets of
high-precision annotated CT data from multiple vendors and countries to train
the model and achieved Cl-DICE, Cl-Recall, and Recall values of 0.892, 0.861,
0.924 for CTPA data and 0.925, 0.903, 0.949 for NCCT data. This shows that our
model has achieved good performance in both accuracy and completeness of
pulmonary vessel segmentation. We finally conducted a clinical visual
assessment on an independent external test dataset. The average score for
accuracy and robustness, branch abundance, assistance for diagnosis and
vascular continuity are 4.26, 4.17, 4.33, 3.83 respectively while the full
score is 5. These results highlight the great potential of this method in
clinical application.
