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

Rina D. Rudyanto, Sjoerd Kerkstra, Eva M. van Rilowort, Catalin Fetita, Pierre-Yves Brillet, Christophe Lefevre, Wenzhe Xue, Xiangjun Zhu, Jianming Liang, İlkay Öksüz, Devrim Unay, Kamuran Kadipasoglu, Raul San Jose Estepar, James C. Ross, George R. Washko, Juan-Carlos Prieto, Marcela Hernandez Hoyos, Maciej Orkisz, Hans Meine, Markus HuellebrandChristina Stoecker, Fernando Lopez Mir, Valery Naranjo, Eliseo Villanueva, Marius Staring, Changyan Xiao, Berend C. Stoel, Anna m Fabijanska, Erik Smistad, Anne C. Elster, Frank Lindseth, Amir Hossein 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 Munoz-Barrutia, Bram van Ginneken

Research output: Contribution to journalArticlepeer-review

129 Citations (Scopus)


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
Pages (from-to)1217-1232
JournalMedical Image Analysis
Issue number7
Early online date23 Jul 2014
Publication statusPublished - Oct 2014


  • Thoracic computed tomography
  • Lung vessels
  • Algorithm comparison
  • Segmentation
  • Challenge


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