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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2015
Author Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, et al.,
Project Establishing Novel MR Criteria for the Assessment of Malignant Glioma Progression
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Original article (peer-reviewed)

Journal IEEE Transactions on Medical Imaging
Volume (Issue) 34(10)
Page(s) 1993 - 2024
Title of proceedings IEEE Transactions on Medical Imaging
DOI 10.1109/TMI.2014.2377694

Open Access

URL http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6975210
Type of Open Access Publisher (Gold Open Access)

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 upto 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.
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