IEEE Transactions on Medical Imaging· 2014Q1
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
- 6,599citations
- Q1SCImago
- 2014year
Short summary
The BRATS benchmark (n=65 scans) showed human raters disagreed on brain tumor segmentation (Dice 74-85%), and while algorithms matched human variability for sub-regions, no single algorithm excelled across all. Fusing algorithms via majority vote improved performance.
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Key points
- Human raters showed considerable disagreement in segmenting brain tumor sub-regions, with Dice scores ranging from 74% to 85%.
- Twenty state-of-the-art segmentation algorithms were evaluated on 65 multi-contrast MR scans.
- Different algorithms performed best on different tumor sub-regions, matching human inter-rater variability.
- No single algorithm ranked highest across all tumor sub-regions simultaneously.
- Fusing multiple algorithms using a hierarchical majority vote consistently outperformed individual algorithms.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Transactions on Medical Imaging, 2014 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science