The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Kistler, Michael; Bonaretti, Serena; Pfahrer, Marcel; Niklaus, Roman; Büchler, Philippe

doi:10.2196/jmir.2930

Cited by 267 publications

(125 citation statements)

References 26 publications

(25 reference statements)

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“…We used two different platforms: the Virtual Skeleton Database (VSD), hosted at the University of Bern, and the Multimedia Digital Archiving System (MIDAS), hosted at Kitware [80]. On both systems participants can download annotated training and “blinded” test data, and upload their segmentations for the test cases.…”

Section: Set-up Of the Brats Benchmarkmentioning

confidence: 99%

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Menze

Jakab

Bauer

et al. 2015

IEEE Trans. Med. Imaging

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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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Section: Set-up Of the Brats Benchmarkmentioning

confidence: 99%

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Menze

Jakab

Bauer

et al. 2015

IEEE Trans. Med. Imaging

4,428

2,856

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show abstract

“…The challenge was launched in February 2015 and potential participants were contacted directly following an extensive literature review on stroke segmentation or via suitable mailing lists. The training datasets for SISS and SPES were released in April 2015 using the the SICAS Medical Image Repository (SMIR) platform 2 (Kistler et al, 2013). The participants were able to download the testing datasets from September 14, 2015, and had to submit their results within a week.…”

Section: Setup Of Islesmentioning

confidence: 99%

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Maier

Menze

Gablentz

et al. 2017

Medical Image Analysis

435

322

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Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).

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“…In this study, we have concentrated on developing and validating an automated method for a single MRI modality, FLAIR, that could be readily translated for clinical use. Future automated methods are likely to incorporate information from multimodal clinical MRI as in the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) database studies [7–9] and also include perfusion and diffusion imaging to detect tumour tissue subtypes (e.g. necrosis, active tumour, infiltrative tumour, oedema) [10].…”

Section: Introductionmentioning

confidence: 99%

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

et al. 2016

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PurposeWe propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI).MethodsThe method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour.ResultsThe proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively.ConclusionsThis provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

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The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Cited by 267 publications

References 26 publications

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

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