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

Rudyanto, Rina D. and Kerkstra, Sjoerd and van Rikxoort, Eva M. and Fetita, Catalin and Brillet, Pierre-Yves and Lefevre, Christophe and Xue, Wenzhe and Zhu, Xiangjun and Liang, Jianming and Oksuz, Ilkay and Ünay, Devrim and Kadipasaoglu, Kamuran and Estépar, Raúl San José and Ross, James C. and Washko, George R. and Prieto, Juan-Carlos and Hoyos, Marcela Hernández and Orkisz, Maciej and Meine, Hans and Hüllebrand, Markus and Stöcker, Christina and Mir, Fernando Lopez and Naranjo, Valery and Villanueva, Eliseo and Staring, Marius and Xiao, Changyan and Stoel, Berend C. and Fabijanska, Anna and Smistad, Erik and Elster, Anne C. and Lindseth, Frank and Foruzan, Amir Hossein and Kiros, Ryan and Popuri, Karteek and Cobzas, Dana and Jimenez-Carretero, Daniel and Santos, Andres and Ledesma-Carbayo, Maria J. and Helmberger, Michael and Urschler, Martin and Pienn, Michael and Bosboom, Dennis G.H. and Campo, Arantza and Prokop, Mathias and de Jong, Pim A. and Ortiz-de-Solorzano, Carlos and Muñoz-Barrutia, Arrate and van Ginneken, Bram Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis, 18 (7). pp. 1217-1232. ISSN 1361-8415 (2014)

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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.

Item Type: Article
Identification Number: 10.1016/j.media.2014.07.003
Uncontrolled Keywords: Thoracic Computed Tomography; Lung Vessels; Algorithm comparison; Segmentation; Challenge
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Research Area: Computer Science and Applications
Depositing User: Ms T. Iannizzi
Date Deposited: 04 Aug 2014 11:23
Last Modified: 06 Apr 2016 08:20
URI: http://eprints.imtlucca.it/id/eprint/2268

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