\section{Introduction}

Pulmonary vascular analysis plays an important role in diagnosing and monitoring lung disease, assessing treatment responses, and understanding long-term changes in pulmonary function. Artery-vein (A/V) segmentation in thoracic CT enables detection of emboli, assessment of pulmonary hypertension, and can help evaluate treatment-related vascular changes, such as damage or remodeling after radiotherapy. Anatomical labeling of vascular branches further enables regional quantification and longitudinal analysis of vascular remodeling, which is particularly relevant for studying radiation-induced lung injury.

Despite recent progress in deep learning for medical imaging, automated A/V segmentation and anatomical labeling remain challenging. Arteries and veins exhibit similar intensities in CT, especially in non-contrast scans, and distal branches are thin, tortuous, and prone to discontinuities. Accurate A/V differentiation requires integrating long-range structural context, which conventional CNNs struggle to capture. Anatomical labeling adds an additional layer of complexity, as labels must respect global topology across the entire vascular tree. Progress in this field is further hindered by the absence of publicly available datasets that provide both A/V segmentation and anatomical labels, forcing researchers to combine heterogeneous datasets and limiting generalization across patient populations, disease states, and acquisition protocols.

Existing approaches typically address either A/V segmentation or anatomical labeling in isolation. As a result, they do not produce fully structured vascular trees and are difficult to apply in large-scale or longitudinal studies where anatomically consistent quantification is essential. Furthermore, current datasets lack distal vessel annotations, preventing existing methods from producing complete vascular trees.

In this work, we introduce \textbf{PaSAL} (Pulmonary artery-vein Segmentation and Anatomical Labeling), a unified deep learning pipeline that jointly performs high-resolution A/V segmentation and 19-class anatomical labeling from CT. PaSAL integrates nnU-Net segmentation with a graph-based labeling framework and introduces several new components. These include hierarchical extended vessel targets, topology-aware skeleton reconnection, robust graph extraction and reorientation, and peripheral label propagation. Together, these components enable anatomically coherent predictions across the entire vascular tree. The pipeline is trained on the HiPaS and PTL datasets and evaluated on an external cohort of 63 clinical scans, from patients who underwent lung radiotherapy.

\textbf{Our main contributions are:} (i) the first unified end-to-end pipeline that bridges the gap between raw thoracic CT and structured, 19-class labeled vascular trees to enable anatomically consistent longitudinal analysis; (ii) new data engineering components and target refinements designed to address the distal annotation gap, including hierarchical extended vessel targets and an MST-inspired skeleton reconnection strategy for robust tree construction; (iii) a comprehensive validation on a clinical cohort of 63 scans from lung cancer patients, demonstrating system robustness across diverse pathologies and significant post-radiotherapy remodeling; and (iv) an analysis revealing a decoupling between standard overlap metrics and expert-perceived clinical quality in high-performance regimes, challenging current evaluation paradigms for complex vascular structures.

An overview of the PaSAL prediction pipeline is shown in Figure~\ref{fig:prediction-pipeline}. Details on data preprocessing, model architectures, and post-processing steps are provided in the Methods section, with additional implementation details in the Appendix.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.95\linewidth]{Figures/Methodology/prediction pipeline.PNG}
    \caption{Overview of the PaSAL prediction pipeline. Each dark blue block represents one of the four main modules: (a) hierarchical segmentation, (b) preprocessing for anatomical labeling, (c) IPGN-based anatomical labeling, and (d) label propagation to peripheral vessels.}
    \label{fig:prediction-pipeline}
\end{figure}
