The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).
复制标题
多模式的脑肿瘤图像分割基准（Brats）。
DOI：
10.1109/tmi.2014.2377694
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发表时间：
2015-10
影响因子：
10.6
通讯作者：
Van Leemput K
中科院分区：
文献类型：
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作者：
Menze BH;Jakab A;Bauer S;Kalpathy-Cramer J;Farahani K;Kirby J;Burren Y;Porz N;Slotboom J;Wiest R;Lanczi L;Gerstner E;Weber MA;Arbel T;Avants BB;Ayache N;Buendia P;Collins DL;Cordier N;Corso JJ;Criminisi A;Das T;Delingette H;Demiralp Ç;Durst CR;Dojat M;Doyle S;Festa J;Forbes F;Geremia E;Glocker B;Golland P;Guo X;Hamamci A;Iftekharuddin KM;Jena R;John NM;Konukoglu E;Lashkari D;Mariz JA;Meier R;Pereira S;Precup D;Price SJ;Raviv TR;Reza SM;Ryan M;Sarikaya D;Schwartz L;Shin HC;Shotton J;Silva CA;Sousa N;Subbanna NK;Szekely G;Taylor TJ;Thomas OM;Tustison NJ;Unal G;Vasseur F;Wintermark M;Ye DH;Zhao L;Zhao B;Zikic D;Prastawa M;Reyes M;Van Leemput K
在本文中，我们报告了与MICCAI 2012和2013会议一起组织的多模态脑肿瘤图像分割基准（BRATS）的设置和结果。20个国家的最先进的肿瘤分割算法被应用到一组65多对比度MR扫描的低级别和高级别胶质瘤患者手动注释由多达4个评级和65个可比的扫描使用肿瘤图像模拟软件生成。定量评估显示，在分割各种肿瘤子区域（Dice评分范围为74%-85%）方面，人类评分员之间存在相当大的分歧，这说明了这项任务的难度。我们发现，不同的算法对不同的子区域效果最好（达到与人类评分员间变异性相当的性能），但没有一种算法同时在所有子区域中排名第一。融合几个好的算法，使用分层多数表决产生的分割，始终排名高于所有个别算法，表明剩余的机会，进一步改进方法。BRATS图像数据和手册注释继续通过在线评估系统作为持续的基准资源公开提供。
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.
最小化基于迭代斑块的标签融合的关节标签的关节风险。
DOI：
10.1007/978-3-642-40760-4_69
发表时间：
2013
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
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作者：
Wu, Guorong;Wang, Qian;Liao, Shu;Zhang, Daoqiang;Nie, Feiping;Shen, Dinggang
通讯作者：
Shen, Dinggang