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"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 Kadipaşaoglu, Raúl San José Estépar, James C. Ross, George R. Washko, Juan-Carlos Prieto, Marcela Hernández Hoyos, Maciej Orkisz, Hans Meine, Markus Hüllebrand, Christina Stöcker, Fernando Lopez Mir, Valery Naranjo, Eliseo Villanueva, Marius Staring, Changyan Xiao, Berend C. Stoel, Anna 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 Muñoz-Barrutia and Bram van Ginneken

Abstract

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 http://vessel12.grand-challenge.org. 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.

 

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Copyright © 2014 by the authors. Published version © 2014 by Elsevier Science. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@article{Rudyanto:2014,
author = "{Rina D. Rudyanto and Sjoerd Kerkstra and Eva M. van Rikxoort and Catalin Fetita and Pierre-Yves Brillet and Christophe Lefevre and Wenzhe Xue and Xiangjun Zhu and Jianming Liang and Ilkay Öksüz and Devrim Ünay and Kamuran Kadipaşaoglu and Raúl San José Estépar and James C. Ross and George R. Washko and Juan-Carlos Prieto and Marcela Hernández Hoyos and Maciej Orkisz and Hans Meine and Markus Hüllebrand and Christina Stöcker and Fernando Lopez Mir and Valery Naranjo and Eliseo Villanueva and Marius Staring and Changyan Xiao and Berend C. Stoel and Anna Fabijanska and Erik Smistad and Anne C. Elster and Frank Lindseth and Amir Hossein Foruzan and Ryan Kiros and Karteek Popuri and Dana Cobzas and Daniel Jimenez-Carretero and Andres Santos and Maria J. Ledesma-Carbayo and Michael Helmberger and Martin Urschler and Michael Pienn and Dennis G. H. Bosboom and Arantza Campo and Mathias Prokop and Pim A. de Jong and Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia and Bram van Ginneken}",
title = "{Comparing algorithms for automated vessel segmentation in Computed Tomography scans of the lung: The VESSEL12 study}",
journal = "{Medical Image Analysis}",
volume = "{18}",
number = "{7}",
pages = "{1217 - 1232}",
month = "{October}",
year = "{2014}",
}

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