MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

Adriënne Mirjam Mendrik; Koen L Vincken; Hugo J Kuijf; Marcel<p> Breeuwer; Willem Bouvy; Jeroen de Bresser; Amir Alansary; Marleen de Bruijne; Aaron Carass; Ayman El-Baz; Amod Jog; Ranveer Katyal; Ali R Khan; Fedde van der Lijn; Qaiser  Mahmood; Ryan Mukherjee; Annegreet van Opbroek; Sahil Paneri; Sérgio Pereira; Mikael Persson; Martin Rajchl AND Duygu Sarikaya; Örjan Smedby; Carlos A Silva; Henri A Vrooman; Saurabh Vyas; Chunliang Wang; Liang Zhao; Geert Jan Biessels; Max A Viergever

doi:10.1155/2015/813696

MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

Adriënne Mirjam Mendrik, Koen L Vincken, Hugo J Kuijf, Marcel<p> Breeuwer, Willem Bouvy, Jeroen de Bresser, Amir Alansary, Marleen de Bruijne, Aaron Carass, Ayman El-Baz, Amod Jog, Ranveer Katyal, Ali R Khan, Fedde van der Lijn, Qaiser Mahmood, Ryan Mukherjee, Annegreet van Opbroek, Sahil Paneri, Sérgio Pereira, Mikael PerssonMartin Rajchl AND Duygu Sarikaya, Örjan Smedby, Carlos A Silva, Henri A Vrooman, Saurabh Vyas, Chunliang Wang, Liang Zhao, Geert Jan Biessels, Max A Viergever

Department of Computer Science

104 Citations (Scopus)

60 Downloads (Pure)

Abstract

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Original language	English
Article number	813696
Journal	Computational Intelligence and Neuroscience
Volume	2015
Number of pages	16
ISSN	1687-5265
DOIs	https://doi.org/10.1155/2015/813696
Publication status	Published - 2015

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Mendrik, A. M., Vincken, K. L., Kuijf, H. J., Breeuwer, M., Bouvy, W., de Bresser, J., Alansary, A., de Bruijne, M., Carass, A., El-Baz, A., Jog, A., Katyal, R., Khan, A. R., van der Lijn, F., Mahmood, Q., Mukherjee, R., van Opbroek, A., Paneri, S., Pereira, S., ... Viergever, M. A. (2015). MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans. Computational Intelligence and Neuroscience, 2015, [813696]. https://doi.org/10.1155/2015/813696

Mendrik, AM, Vincken, KL, Kuijf, HJ, Breeuwer, M, Bouvy, W, de Bresser, J, Alansary, A, de Bruijne, M, Carass, A, El-Baz, A, Jog, A, Katyal, R, Khan, AR, van der Lijn, F, Mahmood, Q, Mukherjee, R, van Opbroek, A, Paneri, S, Pereira, S, Persson, M, Sarikaya, MRANDD, Smedby, Ö, Silva, CA, Vrooman, HA, Vyas, S, Wang, C, Zhao, L, Biessels, GJ & Viergever, MA 2015, 'MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans', Computational Intelligence and Neuroscience, vol. 2015, 813696. https://doi.org/10.1155/2015/813696

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title = "MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans",

abstract = "Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.",

author = "Mendrik, {Adri{\"e}nne Mirjam} and Vincken, {Koen L} and Kuijf, {Hugo J} and Marcel<p> Breeuwer and Willem Bouvy and {de Bresser}, Jeroen and Amir Alansary and {de Bruijne}, Marleen and Aaron Carass and Ayman El-Baz and Amod Jog and Ranveer Katyal and Khan, {Ali R} and {van der Lijn}, Fedde and Qaiser Mahmood and Ryan Mukherjee and {van Opbroek}, Annegreet and Sahil Paneri and S{\'e}rgio Pereira and Mikael Persson and Sarikaya, {Martin Rajchl AND Duygu} and {\"O}rjan Smedby and Silva, {Carlos A} and Vrooman, {Henri A} and Saurabh Vyas and Chunliang Wang and Liang Zhao and Biessels, {Geert Jan} and Viergever, {Max A}",

year = "2015",

doi = "10.1155/2015/813696",

language = "English",

volume = "2015",

journal = "Computational Intelligence and Neuroscience",

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T1 - MRBrainS challenge

T2 - online evaluation framework for brain image segmentation in 3T MRI scans

AU - Mendrik, Adriënne Mirjam

AU - Vincken, Koen L

AU - Kuijf, Hugo J

AU - Breeuwer, Marcel<p>

AU - Bouvy, Willem

AU - de Bresser, Jeroen

AU - Alansary, Amir

AU - de Bruijne, Marleen

AU - Carass, Aaron

AU - El-Baz, Ayman

AU - Jog, Amod

AU - Katyal, Ranveer

AU - Khan, Ali R

AU - van der Lijn, Fedde

AU - Mahmood, Qaiser

AU - Mukherjee, Ryan

AU - van Opbroek, Annegreet

AU - Paneri, Sahil

AU - Pereira, Sérgio

AU - Persson, Mikael

AU - Sarikaya, Martin Rajchl AND Duygu

AU - Smedby, Örjan

AU - Silva, Carlos A

AU - Vrooman, Henri A

AU - Vyas, Saurabh

AU - Wang, Chunliang

AU - Zhao, Liang

AU - Biessels, Geert Jan

AU - Viergever, Max A

PY - 2015

Y1 - 2015

N2 - Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

AB - Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

U2 - 10.1155/2015/813696

DO - 10.1155/2015/813696

M3 - Journal article

SN - 1687-5265

VL - 2015

JO - Computational Intelligence and Neuroscience

JF - Computational Intelligence and Neuroscience

M1 - 813696

ER -

MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

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