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; I. lkay Öksüz; Devrim Ünay; Kamuran Kadipaşaoǧlu; 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; Bram van Ginneken

doi:10.1016/j.media.2014.07.003

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, I. lkay Öksüz, Devrim Ünay, Kamuran Kadipaşaoǧlu, Raúl San José Estépar, James C. Ross, George R. Washko, Juan Carlos Prieto, Marcela Hernández Hoyos, Maciej Orkisz, Hans Meine, Markus HüllebrandChristina 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, Bram van Ginneken

^*Corresponding author for this work

Institute of Computer Graphics and Vision (7100)

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1217-1232
Number of pages	16
Journal	Medical Image Analysis
Volume	18
Issue number	7
DOIs	https://doi.org/10.1016/j.media.2014.07.003
Publication status	Published - 2014

Keywords

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

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)

Access to Document

10.1016/j.media.2014.07.003

Cite this

Rudyanto, R. D., Kerkstra, S., van Rikxoort, E. M., Fetita, C., Brillet, P. Y., Lefevre, C., Xue, W., Zhu, X., Liang, J., Öksüz, I. L., Ünay, D., Kadipaşaoǧlu, K., Estépar, R. S. J., Ross, J. C., Washko, G. R., Prieto, J. C., Hoyos, M. H., Orkisz, M., Meine, H., ... van Ginneken, B. (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study. Medical Image Analysis, 18(7), 1217-1232. https://doi.org/10.1016/j.media.2014.07.003

Rudyanto, RD, Kerkstra, S, van Rikxoort, EM, Fetita, C, Brillet, PY, Lefevre, C, Xue, W, Zhu, X, Liang, J, Öksüz, IL, Ünay, D, Kadipaşaoǧlu, K, Estépar, RSJ, Ross, JC, Washko, GR, Prieto, JC, Hoyos, MH, Orkisz, M, Meine, H, Hüllebrand, M, Stöcker, C, Mir, FL, Naranjo, V, Villanueva, E, Staring, M, Xiao, C, Stoel, BC, Fabijanska, A, Smistad, E, Elster, AC, Lindseth, F, Foruzan, AH, Kiros, R, Popuri, K, Cobzas, D, Jimenez-Carretero, D, Santos, A, Ledesma-Carbayo, MJ, Helmberger, M, Urschler, M, Pienn, M, Bosboom, DGH, Campo, A, Prokop, M, de Jong, PA, Ortiz-de-Solorzano, C, Muñoz-Barrutia, A & van Ginneken, B 2014, 'Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study', Medical Image Analysis, vol. 18, no. 7, pp. 1217-1232. https://doi.org/10.1016/j.media.2014.07.003

@article{ab8fe0e0002741919d5b1883f02dde70,

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

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.",

keywords = "Algorithm comparison, Lung vessels, Thoracic computed tomography",

author = "Rudyanto, {Rina D.} and Sjoerd Kerkstra and {van Rikxoort}, {Eva M.} and Catalin Fetita and Brillet, {Pierre Yves} and Christophe Lefevre and Wenzhe Xue and Xiangjun Zhu and Jianming Liang and {\"O}ks{\"u}z, {I. lkay} and Devrim {\"U}nay and Kamuran Kadipa{\c s}aoǧlu and Est{\'e}par, {Ra{\'u}l San Jos{\'e}} and Ross, {James C.} and Washko, {George R.} and Prieto, {Juan Carlos} and Hoyos, {Marcela Hern{\'a}ndez} and Maciej Orkisz and Hans Meine and Markus H{\"u}llebrand and Christina St{\"o}cker and Mir, {Fernando Lopez} and Valery Naranjo and Eliseo Villanueva and Marius Staring and Changyan Xiao and Stoel, {Berend C.} and Anna Fabijanska and Erik Smistad and Elster, {Anne C.} and Frank Lindseth and Foruzan, {Amir Hossein} and Ryan Kiros and Karteek Popuri and Dana Cobzas and Daniel Jimenez-Carretero and Andres Santos and Ledesma-Carbayo, {Maria J.} and Michael Helmberger and Martin Urschler and Michael Pienn and Bosboom, {Dennis G.H.} and Arantza Campo and Mathias Prokop and {de Jong}, {Pim A.} and Carlos Ortiz-de-Solorzano and Arrate Mu{\~n}oz-Barrutia and {van Ginneken}, Bram",

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journal = "Medical Image Analysis",

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T1 - Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung

T2 - The VESSEL12 study

AU - Rudyanto, Rina D.

AU - Kerkstra, Sjoerd

AU - van Rikxoort, Eva M.

AU - Fetita, Catalin

AU - Brillet, Pierre Yves

AU - Lefevre, Christophe

AU - Xue, Wenzhe

AU - Zhu, Xiangjun

AU - Liang, Jianming

AU - Öksüz, I. lkay

AU - Ünay, Devrim

AU - Kadipaşaoǧlu, Kamuran

AU - Estépar, Raúl San José

AU - Ross, James C.

AU - Washko, George R.

AU - Prieto, Juan Carlos

AU - Hoyos, Marcela Hernández

AU - Orkisz, Maciej

AU - Meine, Hans

AU - Hüllebrand, Markus

AU - Stöcker, Christina

AU - Mir, Fernando Lopez

AU - Naranjo, Valery

AU - Villanueva, Eliseo

AU - Staring, Marius

AU - Xiao, Changyan

AU - Stoel, Berend C.

AU - Fabijanska, Anna

AU - Smistad, Erik

AU - Elster, Anne C.

AU - Lindseth, Frank

AU - Foruzan, Amir Hossein

AU - Kiros, Ryan

AU - Popuri, Karteek

AU - Cobzas, Dana

AU - Jimenez-Carretero, Daniel

AU - Santos, Andres

AU - Ledesma-Carbayo, Maria J.

AU - Helmberger, Michael

AU - Urschler, Martin

AU - Pienn, Michael

AU - Bosboom, Dennis G.H.

AU - Campo, Arantza

AU - Prokop, Mathias

AU - de Jong, Pim A.

AU - Ortiz-de-Solorzano, Carlos

AU - Muñoz-Barrutia, Arrate

AU - van Ginneken, Bram

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Algorithm comparison

KW - Lung vessels

KW - Thoracic computed tomography

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DO - 10.1016/j.media.2014.07.003

M3 - Article

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SP - 1217

EP - 1232

JO - Medical Image Analysis

JF - Medical Image Analysis

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

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Klinisch-Forensische Bildgebung - Developing methods to standardize computer-assisted medical MRI datas [Original in Deutsch: Klinisch-Forensische Bildgebung] - Standard_MRT

Cite this

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

Abstract

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

Access to Document

Other files and links

Fingerprint

Projects

Klinisch-Forensische Bildgebung - Developing methods to standardize computer-assisted medical MRI datas [Original in Deutsch: Klinisch-Forensische Bildgebung] - Standard_MRT

Cite this