\documentclass{midl} % Include author names

% The following packages will be automatically loaded:
% jmlr, amsmath, amssymb, natbib, graphicx, url, algorithm2e
% ifoddpage, relsize and probably more
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\usepackage{booktabs}
\usepackage{multirow}

% \usepackage{mwe} % to get dummy images

\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\jmlrvolume{-- 355}
\editors{Accepted for publication at MIDL 2026}

\title[3D Vessel Segmentation]{Unlocking 2D Promptable Foundation Models for 3D Vessel Segmentation by Automatic Prompt Generation}

 % Use \Name{Author Name} to specify the name.
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 % if the name has a "von" part, e.g \Name{Jane {de Winter}}.
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% \midlauthor{\Name{Author Name1} \Email{abc@sample.edu}\\
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%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
% \midlauthor{\Name{Author Name1\midljointauthortext{Contributed equally}\nametag{$^{1,2}$}} \orcid{1111-2222-3333-4444} \Email{abc@sample.edu}\\
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% \Name{Author Name2\midlotherjointauthor\nametag{$^{1}$}} \Email{xyz@sample.edu}\\
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% \Name{Author Name5\midlotherjointauthor\nametag{$^{4}$}} \Email{fgh@bar.com}\\
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\midlauthor{\Name{Ziyu Zhang\midljointauthortext{Contributed equally}\nametag{$^{1}$}}  \orcid{0000-0003-1423-7036} \Email{zhangziyu@nju.edu.cn}\\
% \addr $^{1}$ Address 1 \\
\addr $^{1}$ Nanjing University \AND
\Name{Yi Yu\midlotherjointauthor\nametag{$^{2}$}} \orcid{0000-0002-9841-4687} \Email{Yi.Yu@osumc.edu}\\
% \Name{Author Name3\nametag{$^{2}$}} \Email{alphabeta@example.edu}\\
\Name{Yuan Xue\nametag{$^{2}$}} \orcid{0000-0002-5390-9037} \Email{Yuan.Xue@osumc.edu}\\
\addr $^{2}$ The Ohio State University
% \Name{Author Name5\midlotherjointauthor\nametag{$^{4}$}} \Email{fgh@bar.com}\\
% \addr $^{4}$ Address 4
}

\begin{document}

\maketitle

\begin{abstract}
3D vessel segmentation is a core task in medical image analysis, playing a crucial role in disease diagnosis and surgical planning.
While fully supervised 3D segmentation methods rely on costly high-quality annotations, promptable models (e.g., ScribblePrompt) provide a promising alternative with their zero-shot generalization capability for efficient 3D segmentation.
Nevertheless, when directly applied to 3D tasks, these 2D methods require slice-wise prompts, disregarding the continuity of 3D structures and leading to low efficiency.
To address this issue, we propose an innovative method based on automatic prompt generation, which integrates with pre-trained 2D interactive models to achieve efficient 3D vessel segmentation. 
By leveraging spatial continuity and contextual information, our method automatically generates prompts across the entire 3D volume from a single user-provided prompt.
Experiments conducted on public and in-house vessel datasets demonstrate the effectiveness of the proposed method, showing that it achieves segmentation accuracy comparable to or better than state-of-the-art models, while significantly reducing the interaction cost.
\end{abstract}

\begin{keywords}
Vessel Segmentation, Foundation Models, Automatic Prompting, Topology Preservation.
\end{keywords}

\input{sec/1_introduction}
\input{sec/2_method}
\input{sec/3_experiments}
\input{sec/4_conclusion}

\clearpage  % Acknowledgements, references, and appendix do not count toward the page limit (if any)
% Acknowledgments---Will not appear in anonymized version
\midlacknowledgments{Ziyu Zhang was supported in part by the Natural Science Foundation of Jiangsu Province of China (BK20241202), the China Postdoctoral Science Foundation (2024M751394, GZC2024 0691), and the Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB433).}

\bibliography{midl26_355}
\end{document}
