Journal article
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Massachusetts Institute of Technology1
Swiss Federal Institute of Technology Zurich2
University of Bern3
Massachusetts General Hospital/Harvard Medical School4
National Institutes of Health5
Bern University Hospital6
Department of Applied Mathematics and Computer Science, Technical University of Denmark7
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark8
Aalto University9
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 subregions 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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2015 |
| Pages: | 1993-2024 |
| ISSN: | 1558254x and 02780062 |
| Types: | Journal article |
| DOI: | 10.1109/TMI.2014.2377694 |
| ORCIDs: | Van Leemput, Koen |
Algorithms BRATS Benchmark Benchmark testing Benchmarking Biomedical imaging Brain Dice scores Educational institutions Glioma Humans Image segmentation Lesions MICCAI 2012 conference MICCAI 2013 conference MRI Magnetic Resonance Imaging Multimodal Brain Tumor Image Segmentation Benchmark Neuroimaging Oncology/tumor benchmark testing biomedical MRI brain glioma patients hierarchical majority vote human interrater variability image segmentation medical image processing multicontrast MR scans tumor image simulation software tumor segmentation algorithm tumours
