\documentclass{midl} % Include author names
\usepackage{longtable}
\usepackage{booktabs}

\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\jmlrvolume{-- nnn}
\editors{Accepted for publication at MIDL 2026}

\title[PaSAL: Pulmonary A/V Segmentation and Labeling]{PaSAL: A Deep Learning Pipeline for Pulmonary Artery-Vein Segmentation and Anatomical Labeling in Thoracic CT}

 \midlauthor{
  \Name{Jasper Eppink\midljointauthortext{Contributed equally}\nametag{$^{1,2}$}}
  \Email{jasper.eppink@gmail.com}\\
  \Name{Hoel Kervadec\midlotherjointauthor\nametag{$^{2}$}}
  \orcid{0000-0002-6786-7042}
  \Email{h.t.g.kervadec@uva.nl}\\
  \Name{Julian {van Capelleveen}\nametag{$^{1}$}}
  \Email{j.c.vancapelleveen@amsterdamumc.nl}\\
  \Name{Joost Verhoeff\nametag{$^{1}$}}
  \orcid{0000-0001-9673-0793}
  \Email{joost.verhoeff@amsterdamumc.nl}\\
  \Name{Suresh Senan\nametag{$^{1}$}}
  \orcid{0000-0003-3995-2204}
  \Email{s.senan@amsterdamumc.nl}\\
  \Name{Omar Bohoudi\nametag{$^{1}$}}
  \orcid{0000-0003-0315-1034}
  \Email{o.bohoudi@amsterdamumc.nl}\\
  \addr $^{1}$ Amsterdam UMC, Department of Radiation Oncology, Amsterdam, The Netherlands \\
  \addr $^{2}$ University of Amsterdam, Informatics Institute, Amsterdam, The Netherlands\\
  }

\begin{document}

\maketitle

\begin{abstract}
We present PaSAL, a deep learning pipeline for pulmonary artery-vein segmentation and anatomical labeling in thoracic CT. PaSAL combines an nnU-Net-based binary vessel segmentation model with a graph-based anatomical labeling framework that assigns 19 clinically defined vascular classes. The pipeline integrates vessel enhancement, skeletonization, and topology-aware label propagation to produce anatomically coherent outputs.

PaSAL is trained on the HiPaS and PTL public datasets and evaluated on an external set of 63 clinical scans from Amsterdam UMC. On HiPaS, PaSAL achieves Dice scores of $89.5\%$ (arteries) and $88.1\%$ (veins). On PTL, voxel-level anatomical labeling accuracy reaches $90.1\%$ for arteries and $82.7\%$ for veins. Expert review confirms high anatomical plausibility and clinical utility, while showing weak correlation between standard quantitative metrics and perceived quality.

To our knowledge, PaSAL is the first method to jointly perform artery-vein segmentation and anatomical labeling in CT. The results demonstrate robust performance across diverse anatomical presentations, including pre- and post-radiotherapy scans, and establish PaSAL as a useful baseline tool for vascular analysis in medical imaging.
\end{abstract}

\begin{keywords}
Pulmonary artery-vein segmentation, Pulmonary anatomical labeling, Thoracic CT, Deep learning, Graph-based learning
\end{keywords}

\input{Sections/1. Introduction}
\input{Sections/2. Related work}
\input{Sections/3. Methodology}
\input{Sections/4. Results}
\input{Sections/5. Discussion & Conclusion}


\clearpage  % Acknowledgements, references, and appendix do not count toward the page limit (if any)
% Acknowledgments---Will not appear in anonymized version
\midlacknowledgments{The authors thank the Amsterdam UMC Department of Radiation Oncology for providing clinical data and departmental support for this work.}

\bibliography{midl26_333}

\appendix

\input{Sections/6. Appendix}

\end{document}
