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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Menze B.H., Jakab A., Bauer S., Kalpathy-Cramer J., Farahani K., Kirby J., Burren Y., Porz N., Slotboom J., Wiest R., Lanczi L., Gerstner E., Weber M-A., Arbel T., Avants B.B., Ayache N., Buendia P., Collins D.L., Cordier N., Corso J.J., Criminisi A., Das T., Delingette H., Demiralp C., Durst C.R., Dojat M., Doyle S., Festa J., Forbes F., Geremia E., Glocker B., Golland P., Guo X., Hamamci A., Iftekharuddin K.M., Jena R., John N.M., Konukoglu E., Lashkari D., Mariz J.A., Meier R., Pereira S., Precup D., Price S.J., Raviv T.R., Reza S.M.S., Ryan M., Sarikaya D., Schwartz L., Shin H-C., Shotton J., Silva C.A., Sousa N., Subbanna N.K., Szekely G., Taylor T.J., Thomas O.M., Tustison N.J., Unal G., Vasseur F., Wintermark M., Ye D.H., Zhao L., Zhao B., Zikic D., Prastawa M., Reyes M., Van Leemput K.
Institution:
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publisher:
IEEE Engineering in Medicine and Biology Society
Publication Date:
Oct-2015
Journal:
IEEE Trans Med Imaging
Volume Number:
34
Issue Number:
10
Pages:
1993-2024
Citation:
IEEE Trans Med Imaging. 2015 Oct;34(10):1993-2024.
PubMed ID:
25494501
PMCID:
PMC4833122
Appears in Collections:
NAC, NA-MIC, SLICER
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 RR014075/RR/NCRR NIH HHS/United States
R01 EB013565/EB/NIBIB NIH HHS/United States
R15 CA115464/CA/NCI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Menze B.H., Jakab A., Bauer S., Kalpathy-Cramer J., Farahani K., Kirby J., Burren Y., Porz N., Slotboom J., Wiest R., Lanczi L., Gerstner E., Weber M-A., Arbel T., Avants B.B., Ayache N., Buendia P., Collins D.L., Cordier N., Corso J.J., Criminisi A., Das T., Delingette H., Demiralp C., Durst C.R., Dojat M., Doyle S., Festa J., Forbes F., Geremia E., Glocker B., Golland P., Guo X., Hamamci A., Iftekharuddin K.M., Jena R., John N.M., Konukoglu E., Lashkari D., Mariz J.A., Meier R., Pereira S., Precup D., Price S.J., Raviv T.R., Reza S.M.S., Ryan M., Sarikaya D., Schwartz L., Shin H-C., Shotton J., Silva C.A., Sousa N., Subbanna N.K., Szekely G., Taylor T.J., Thomas O.M., Tustison N.J., Unal G., Vasseur F., Wintermark M., Ye D.H., Zhao L., Zhao B., Zikic D., Prastawa M., Reyes M., Van Leemput K. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015 Oct;34(10):1993-2024. PMID: 25494501. PMCID: PMC4833122.
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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.

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