The multimodal brain tumor image segmentation benchmark (BRATS)
Open accessAbstract
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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Different lesion appearances and annotation targets have been reported across tumor benchmarks.
“different lesion appearances and annotation targets have been reported across tumor benchmarks [3,8,23]”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [23]
Tumor segmentation is less stable because lesions are not tied to a fixed anatomical shape or location and may be small, heterogeneous, or weakly contrasted.
“Tumor segmentation is less stable because lesions are not tied to a fixed anatomical shape or location and may be small, heterogeneous, or weakly contrasted [3,8,23].”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [23]
Among expert annotators there is often considerable variability.
“Among expert annotators there is often considerable variability206,207.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [207]
The BraTS 2018 training dataset included 285 cases (210 HGG and 75 LGG), each with four 3D MRI modalities (T1, T1c, T2 and FLAIR) rigidly aligned, resampled to 1x1x1 mm isotropic resolution and skull-stripped.
“This year, BraTS 2018 training dataset included 285 cases (210 HGG and 75 LGG), each with four 3D MRI modalities (T1, T1c, T2 and FLAIR) rigidly aligned, resampled to 1x1x1 mm isotropic resolution and skull-stripped.”
From 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization (2019) · cites this paper as [18]
The BraTS 2018 testing dataset contained 191 cases, allowed only a single submission, and was used to calculate the final challenge ranking.
“The testing dataset (191 cases) allowed only a single submission, and was used to calculate the final challenge ranking.”
From 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization (2019) · cites this paper as [18]
The BraTS 2018 validation dataset contained 66 cases and allowed multiple submissions, and was designed for intermediate evaluations.
“The validation dataset (66 cases) allowed multiple submissions and was designed for intermediate evaluations.”
From 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization (2019) · cites this paper as [18]
The preliminary federation model was already within the intra- and inter-rater variability range for this use-case (20% and 28%, respectively), so any further improvements for the full federation consensus model would be expected to be minimal.
“Further noting that the preliminary federation model was already within the intra- and inter-rater variability range for this use-case (20% and 28%, respectively)47, any further improvements for the full federation consensus model would be expected to be minimal35–38.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
The complexity of boundary detection drops when moving from smaller to larger sub-compartments, i.e., from enhancing tumor (ET) to tumor core (TC), and then to whole tumor (WT).
“The complexity of boundary detection drops when moving from smaller to larger sub-compartments, i.e., from ET to TC, and then to WT35–38.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
Varying characteristics of the multi-parametric MRI data due to scanner hardware and acquisition protocol differences were handled at each collaborating site via established harmonized preprocessing pipelines.
“Moreover, varying characteristics of the mpMRI data due to scanner hardware and acquisition protocol differences33,34 were handled at each collaborating site via established harmonized preprocessing pipelines35–39.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
Model ensembles perform better than a single off-the-shelf neural network for the task of detecting glioblastoma sub-compartment boundaries.
“Additionally, we used a single off-the-shelf neural network architecture for training, but it has been shown that model ensembles perform better for the task at hand35–38, and it remains to be explored how such a strategy could be explored in a federated study.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
The BraTS challenge provides the largest publicly available and manually-curated mpMRI dataset of diffuse glioma patients and a harmonized preprocessing pipeline to handle inter-site acquisition protocol differences.
“The BraTS challenge35–38, seeking methodological advancements in the domain of neuro-oncology, has been providing the community with (i) the largest publicly available and manually-curated mpMRI dataset of diffuse glioma patients (an example of which is illustrated in Fig. 1b), and (ii) a harmonized preprocessing pipeline51,70,71 to handle differences in inter-site acquisition protocols.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
The harmonized data preprocessing protocol defined by the BraTS challenge accounts for inter-site acquisition protocol variations, such as 3D vs. 2D axial plane acquisitions.
“This functionality follows the harmonized data preprocessing protocol defined by the BraTS challenge35–38, as described below. This would allow accounting for inter-site acquisition protocol variations, e.g., 3D vs. 2D axial plane acquisitions.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
Running an ensemble of multiple models is typically done in academic research projects.
“which is typically done in academic research projects35–38”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
Ensuring strict compliance with the preprocessing and annotation protocol for the generation of reference standards was vital for the model to learn correct information during training.
“Following the precise definition of our problem statement35–38, ensuring strict compliance with the preprocessing and annotation protocol for the generation of reference standards was vital for the model to learn correct information during training.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [35]
Eaton-Rosene et al. proposed a generalizable Bayesian Neural Network technique for uncertainty quantification in semantic segmentation, evaluated on the BraTS 2017 dataset of 285 subjects with high- or low-grade gliomas.
“Eaton-Rosene et al. [75] proposed a generalizable technique for quantifying uncertainty with Bayesian Neural Networks for semantic segmentation on the BraTS 2017 dataset [76], including 285 subjects with high-or low-grade gliomas.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [76]
Chen et al. (2019c) report that for tumor segmentation in the BraTS dataset (120 training and 50 test MR scans), their method achieves six and seven points increase in Dice over a supervised model when 20 and 50 labeled MR images are used for training.
“For tumor segmentation in the BraTS dataset Menze et al. (2014) (120 training and 50 test MR scans), the authors report six and seven points increase in Dice over supervised model when 20 and 50 labeled MR images are used for training.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Menze et al. (2014)
Myronenko (2018) method outperforms other modifications such as CRF post-processing and test-time data augmentation, winning first place in the 2018 BraTS challenge.
“The suggested method outperforms other modifications such as CRF post-processing as well as sophisticated test-time data augmentation, winning the first place in the 2018 BraTS challenge”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Menze et al. (2014)
Kamnitsas et al. (2017) 3D generalization on BraTS dataset (274 training and 110 testing) leads to marginal performance gains in Dice: 3.7% over Random Forests, 0.3% over an ensemble method, and 0.7% over their proposed architecture.
“their 3D generalization on BraTS dataset Menze et al. (2014) (274 training and 110 testing) leads to marginal performance gains in Dice, i.e. 3.7% over Random Forests, 0.3% over an ensemble method, and merely 0.7% over their proposed architecture”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Menze et al. (2014)
On BraTS 2015 (220 multi-modal MR images), 3D RNN-CRF slightly improves Dice for tumor segmentation from 73.5% ± 10.5% to 73.8% ± 10.5% based on a split of 85%/15% (187 training and 33 testing).
“And on BraTS 2015 Menze et al. (2014), consisting of 220 multi-modal MR images of brain tumors, 3D RNN-CRF slightly improves the Dice for tumor segmentation from 73.5% ± 10.5% to 73.8% ± 10.5% based upon a split of 85%/15% (187 training and 33 testing).”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Menze et al. (2014)
BraTS is a benchmark dataset for brain tumor segmentation.
“Brain Tumor Image Segmentation Benchmark (BraTS) [104] is a benchmark dataset for brain tumor segmentation.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [104]
BraTS contains brain MRIs acquired by various scanners from around 19 independent institutions.
“It contains brain MRIs acquired by various scanners from around 19 independent institutions.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [104]
Existing automatic and semi-automatic brain tumor segmentation methods can be broadly categorized as generative model based or discriminative model based methods.
“The existing automatic and semi-automatic brain tumor segmentation methods can be broadly categorized as either generative model based or discriminative model based methods (Menze et al., 2015).”
From A deep learning model integrating FCNNs and CRFs for brain tumor segmentation (2018) · cites this paper as Menze et al., 2015
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Research works printed but not linked: 6
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“[96] 2022 Detect brain tumor segmentation, using representative clinical datasets Cross-device Centralized Decentralized Real U-Net CFA FedPer Synchronous Asynchronous Random BRATS [167] Private [88] 2022 Detect misconduct model on specific sites from any learning iteration Cross-device Centralized Decentralized Simul…”
MethodInfluentialSelf-supervised learning for medical image analysis using image context restoration
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
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Segment anything model for medical images?
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- Authors
- 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.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- University of Bern (CH)
- National Institutes of Health (US)
- University Hospital of Bern (CH)
- Harvard University (US)
- McGill University (CA)
- University of Pennsylvania (US)
- Infotech Soft (United States) (US)
- University at Buffalo, State University of New York (US)
- Microsoft Research (United Kingdom) (GB)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- University of Virginia (US)
- University of Minho (PT)
- Imperial College London (GB)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Columbia University (US)
- Old Dominion University (US)
- University of Miami (US)
- Ben-Gurion University of the Negev (IL)
- University Hospital Heidelberg (DE)
- Purdue University West Lafayette (US)
- GE Global Research (United States) (US)
- Corresponding author
- Menze, Bjoern H.
- ORCID
- Menze, Bjoern H.
- Farahani, Keyvan
- Kirby, Justin
- Porz, Nicole
- Slotboom, Johannes
- Wiest, Roland
- Gerstner, Elizabeth
- Arbel, Tal
- Avants, Brian B.
- Ayache, Nicholas
- Buendia, Patricia
- Collins, D. Louis
- Cordier, Nicolas
- Corso, Jason J.
- Criminisi, Antonio
- Das, Tilak
- Durst, Christopher R.
- Dojat, Michel
- Doyle, Senan
- Festa, Joana
- Forbes, Florence
- Glocker, Ben
- Golland, Polina
- Meier, Raphael
- Precup, Doina
- Price, Stephen J.
- Raviv, Tammy Riklin
- Ryan, Michael
- Silva, Carlos A.
- Sousa, Nuno
- Thomas, Owen M.
- Tustison, Nicholas J.
- Wintermark, Max
- Ye, Dong Hye
- Zhao, Liang
- Zhao, Binsheng
- Prastawa, Marcel
- Reyes, Mauricio
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2015-10
- Volume
- 34
- Issue
- 10
- Pages
- 1993-2024
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC4833122
- OpenAlex
- W1641498739
- Cited by
- 6,781
- References
- 132
- Citations by year
- 2026: 863 · 2025: 937 · 2024: 899 · 2023: 845 · 2022: 791 · 2021: 709 · 2020: 612 · 2019: 468 · 2018: 310 · 2017: 161 · 2016: 120 · 2015: 49 · 2014: 16
- FWCI
- 9.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ODU Digital Commons (Old Dominion University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Science Foundation
- National Institute for Health and Care Research (NIHR/CS/009/011)
- European Commission (600841)
- Academy of Finland
- Tekes
- Lundbeckfonden (R141-2013-13117)
- Krebsliga Schweiz
- Technische Universität München
- National Institutes of Health
- Fundação para a Ciência e a Tecnologia
- National Cancer Institute (U01CA154601, R15CA115464)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015902, U54EB005149, R01EB013565)
- National Center for Research Resources (P41-RR-14075, P41RR014075, P41RR013218, P41 RR13218)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- brain tumor segmentation, glioma, multimodal MRI, benchmark, interobserver variability, algorithm fusion, majority voting, manual annotation
- MeSH terms
- Algorithms, Glioma, Humans, Magnetic Resonance Imaging, Benchmarking, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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