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MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Overview of attention for article published in Computational Intelligence & Neuroscience, December 2015
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Title
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Published in
Computational Intelligence & Neuroscience, December 2015
DOI 10.1155/2015/813696
Pubmed ID
Authors

Adriënne M. Mendrik, Koen L. Vincken, Hugo J. Kuijf, Marcel Breeuwer, Willem H. 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, Duygu Sarikaya, Örjan Smedby, Carlos A. Silva, Henri A. Vrooman, Saurabh Vyas, Chunliang Wang, Liang Zhao, Geert Jan Biessels, Max A. Viergever

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 164 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 163 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 30%
Researcher 32 20%
Student > Master 18 11%
Student > Doctoral Student 8 5%
Other 7 4%
Other 18 11%
Unknown 31 19%
Readers by discipline Count As %
Computer Science 35 21%
Engineering 33 20%
Medicine and Dentistry 19 12%
Neuroscience 16 10%
Agricultural and Biological Sciences 4 2%
Other 16 10%
Unknown 41 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 January 2016.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from Computational Intelligence & Neuroscience
#866
of 1,218 outputs
Outputs of similar age
#337,554
of 395,463 outputs
Outputs of similar age from Computational Intelligence & Neuroscience
#20
of 23 outputs
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