The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
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The multimodal.pdf
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4.88 MB
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Author(s)
Menze, H. Bjoern
Jakab, Andras
Bauer, Stefan
Kalpathy-Cramer, Jayashree
Farahani, Keyvan
Kirby, Justin
Burren, Yuliya
Porz, Nicole
Slotboom, Johannes
Wiest, Roland
Date Issued
October 2015
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Menze, Bjoern H. et al. “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).” IEEE Transactions on Medical Imaging 34, 10 (October 2015): 1993–2024 © 2015 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TMI.2014.2377694
