The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13)
Issue

Title
Rating
Authors
Reviews
Status
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MAP–Based Framework for Segmentation of MR Brain Images Based on Visual Appearance and Prior Shape
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Alansary A., Soliman A., Khalifa F., Elnakib A., Mostapha M., Nitzken M., Casanova M., El-Baz A.
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Accepted for publication
Multi-Atlas Brain MRI Segmentation with Multiway Cut
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Sarikaya D., Zhao L., Corso J.J.
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Accepted for publication
Automatic Brain Tissue Segmentation of Multi-sequence MR Images Using Random Decision Forests
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Pereira S., Festa J., Mariz J.A., Sousa N., Silva C.A.
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Accepted for publication
Auto-kNN: Brain Tissue Segmentation using Automatically Trained k-Nearest-Neighbor Classification
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Vrooman H., Van der Lijn F., Niessen W.
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Accepted for publication
Gaussian Intensity Model with Neighborhood Cues for Fluid-Tissue Categorization of Multi-Sequence MR Brain Images
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Katyal R., Paneri S., Kuse M.
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Accepted for publication
Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images
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R M.P., R S.S.K.
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Accepted for publication
Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)
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Mahmood Q., Alipoor M., Chodorowski A., Mehnert1 A., Persson M.
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Accepted for publication
Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification
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Van Opbroek A., Van der Lijn F., De Bruijne M.
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Accepted for publication
Multi-Atlas-based Segmentation with Hierarchical Max-Flow
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Rajchl M., Baxter J.S.H., Yuan J., Peters T.M., Khan A.R.
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Accepted for publication
MR Brain Segmentation using Decision Trees
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Jog A., Roy S., Prince J.L., Carass A.
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Accepted for publication
Automated Walks using Machine Learning for Segmentation
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Vyas S., Burlina P., Kleissas D., Mukherjee R.
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Accepted for publication
Fully automatic brain segmentation using model-guided level sets and skeleton-based models
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Wang C., Smedby Ã.
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Accepted for publication

The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13) workshop was held on September 26, 2013 in Nagoya, Japan. It was organized by dr. Adriënne M. Mendrik of the Image Sciences Institute (UMC Utrecht, the Netherlands). The aim of the MRBrainS challenge was to compare (semi-)automatic algorithms for segmentation of grey matter, white matter and cerebrospinal fluid on multi-sequence (T1-weighted, T1-weighted inversion recovery and FLAIR) 3 Tesla MRI scans of the brain. The challenge consisted of an off-site and on-site part. For the off-site part, teams could register on the MRBrainS website, download 5 training datasets (MRI scans + manual segmentations) and 12 test datasets (only MRI scans), train their algorithm, apply it to the test datasets and submit their results. In total 86 teams registered on the website, of which 58 teams signed the confidentiality agreement and downloaded the data. Twelve teams submitted the obtained results and a workshop paper for the MICCAI MRBrainS13 workshop with a description of their algorithm. The workshop papers are available here.
This issue was created the 10-18-2013, the paperdue date is 10-31-2013, the decision date is 10-31-2013, the publication date is 10-31-2013
Eleven teams presented their results at the MRBrainS13 workshop at the MICCAI and applied their algorithm to three additional datasets during the workshop (on-site part of the challenge), the results of the workshop challenge can be found on the website: http://mrbrains13.isi.uu.nl/workshop.php.

The challenge is still open for new submissions, more information about the MRBrainS challenge can be found at http://mrbrains13.isi.uu.nl.


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