Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study

Rina D. Rudyanto, Sjoerd Kerkstra, Eva M. van Rikxoort, Catalin Fetita, Pierre Yves Brillet, Christophe Lefevre, Wenzhe Xue, Xiangjun Zhu, Jianming Liang, Ilkay Öksüz, Devrim Ünay, Kamuran Kadipaşaoğlu, Raúl San J osé Estépar, James C. Ross, George R. Washko, Juan Carlos Prieto, Marcela H ernández Hoyos, Maciej Orkisz, Hans Meine, Markus HüllebrandChristina Stöcker, Fernando L opez Mir, Valery Naranjo, Eliseo Villanueva, Marius Staring, Changyan Xiao, Berend C. Stoel, Anna Fabijanska, Erik Smistad, Anne C. Elster, Frank Lindseth, Amir H ossein Foruzan, Ryan Kiros, Karteek Popuri, Dana Cobzas, Daniel Jimenez-Carretero, Andres Santos, Maria J. Ledesma-Carbayo, Michael Helmberger, Martin Urschler, Michael Pienn, Dennis G H Bosboom, Arantza Campo, Mathias Prokop, Pim A. de Jong, Carlos Ortiz-de-Solorzano, Arrate Muñoz-Barrutia, Bram van Ginneken

    Research output: Contribution to journalArticlepeer-review

    128 Scopus citations


    The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

    Original languageEnglish (US)
    Pages (from-to)1217-1232
    Number of pages16
    JournalMedical image analysis
    Issue number7
    StatePublished - Oct 2014


    • Algorithm comparison
    • Lung vessels
    • Thoracic computed tomography

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design


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