\section{Discussion}

This work introduced PaSAL, a fully automated pipeline for pulmonary artery-vein segmentation and anatomical labeling in thoracic CT. By combining hierarchical nnU-Net segmentation with IPGN-based graph labeling, PaSAL produces structured vascular representations suitable for anatomical and longitudinal analyses.


\subsection{Interpretation of Results}

PaSAL achieves high performance at level $[A^3, V^3]$, with Dice, Sensitivity, and Precision consistently above $90\%$ for arteries and slightly lower for veins. Arterial predictions benefit from stronger morphological priors and more stable centerline extraction, whereas peripheral venous branches remain challenging. Expert ratings (3.4-3.9/5) confirm that the outputs are clinically meaningful across heterogeneous anatomies, including post-treatment scans, indicating that the pipeline is viable for downstream pulmonary research.

A central finding of this study is that no meaningful correlation was observed between expert-perceived quality and conventional overlap metrics once these metrics are already high within the modeled anatomical scope. This aligns with recent observations~\cite{melba:2023:002:kofler} and suggests that marginal improvements in overlap or distance metrics do not necessarily translate into perceived clinical usefulness for central pulmonary vessels. As expert assessment also reflects aspects beyond what is captured by voxelwise metrics alone, expert-based or task-based evaluations remain essential for assessing real-world applicability.

\subsection{Strengths and Novelty}

PaSAL jointly performs artery-vein separation and anatomical labeling, a combination rarely addressed in prior work. To our knowledge, it is the first pipeline to integrate and extend multiple public datasets to produce coherent, labeled vascular trees suitable for downstream analysis. Its modular design enables independent updates to segmentation and labeling components, making PaSAL a flexible foundation for pulmonary vascular research.

\subsection{Limitations}

Several limitations merit discussion.  
(1) Arteries and veins were segmented with separate models, potentially introducing inconsistencies in ambiguous regions; a unified multiclass model may improve spatial coherence.  
(2) Training partially relied on automatically extended HiPaS labels, which introduce local imperfections, particularly in distal vessels.  
(3) The training data lacked acquisition metadata, preventing protocol-stratified analysis and limiting interpretability across heterogeneous scans.  
(4) The multi-stage pipeline (segmentation, skeletonization, graph extraction, labeling) is susceptible to error propagation and depends on heuristic thresholds.  
(5) The loss function does not explicitly prioritize distal vessels; topology-aware objectives~\cite{chu2025deep} may improve performance on small-caliber branches.  
(6) Anatomical labeling was restricted to $[A^3, V^3]$, with deeper branches inferred heuristically, which may induce errors in densely branched regions.

\subsection{Future Work}

Future work includes integrating acquisition metadata for contrast-aware preprocessing and protocol-stratified evaluation; developing unified multi-task architectures that jointly predict masks, centerlines, and labels to reduce error propagation; and exploring topology-aware losses to better capture peripheral vessels. Extending PaSAL to incorporate oncological context (e.g., dose maps, tumor masks) may enhance robustness to altered anatomy and support longitudinal vascular studies.


\section{Conclusion}

PaSAL provides a fully automated, clinically viable approach for pulmonary artery-vein segmentation and anatomical labeling in thoracic CT. Evaluations on public datasets and a radiotherapy cohort show that the pipeline produces anatomically coherent vascular trees with strong quantitative performance and expert-confirmed usability, demonstrating that structured pulmonary vascular analysis is feasible on realistic clinical data.

A key conclusion of this work is that improvements in conventional segmentation metrics do not necessarily yield better expert-perceived or clinically relevant outputs once performance is already high within the anatomical scope covered by available ground truth. This highlights the need for evaluation frameworks that go beyond overlap-based metrics and emphasizes that pipelines intended for clinical or research use must be validated in terms of anatomical correctness and practical usability. Despite limitations such as incomplete metadata, label imperfections, and the multi-stage architecture, PaSAL provides a solid foundation for further development of unified and clinically oriented pulmonary vascular analysis methods.