BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.
BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.
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DOI：
10.1016/j.neuroimage.2016.07.018
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发表时间：
2016-11-01
期刊：
影响因子：
5.7
通讯作者：
Jenkinson M
中科院分区：
文献类型：
--
作者：
Griffanti L;Zamboni G;Khan A;Li L;Bonifacio G;Sundaresan V;Schulz UG;Kuker W;Battaglini M;Rothwell PM;Jenkinson M
鉴于在患有几种神经和血管疾病的患者以及老年健康受试者中存在这些MRI结果，越来越需要可靠地定量推定血管来源的白色高信号（WMH）。我们提出了BIANCA（脑强度异常分类算法），一个完全自动化的，监督的方法WMH检测，基于k-近邻（k-NN）算法。相对于以前的基于k-NN的分割方法，BIANCA提供了不同的选项来加权空间信息，局部空间强度平均，以及不同的选项来选择训练点的数量和位置。BIANCA是多模式和高度灵活的，因此用户可以根据他们的协议和特定需求调整工具。我们在具有不同MRI方案和患者人群的两个数据集上优化和验证了BIANCA（“主要是神经退行性疾病”和“主要是血管性疾病”队列）。BIANCA首先对每个数据集的图像子集进行了优化，包括与手动分割的WMH掩模的重叠和体积一致性。使用BIANCA提取的体积（使用优化的选项集），从手动掩模提取的体积和视觉评级之间的相关性表明，BIANCA是手动分割的有效替代方案。然后将优化的选项集应用于整个队列，由此产生的WMH体积估计显示出与视觉评级和年龄的良好相关性。最后，我们进行了重现性测试，以评估BIANCA的鲁棒性，并将BIANCA的性能与现有方法进行了比较。我们的研究结果表明，BIANCA，这将是免费提供的FSL包的一部分，是一个可靠的方法自动WMH分割在大型横断面队列研究。BIANCA是一种用于自动分割白色高信号的新工具。BIANCA是多模式的，灵活的，计算精简的，强大的，免费的。我们在两种不同的MRI方案和人群中优化和验证了BIANCA。用BIANCA推导的WMH体积与视觉等级和年龄表现出良好的相关性。BIANCA在大型横断面队列研究中具有良好的应用前景。
Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a “predominantly neurodegenerative” and a “predominantly vascular” cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. BIANCA is a new tool for automated segmentation of white matter hyperintensities. BIANCA is multimodal, flexible, computationally lean, robust, freely available. We optimised and validated BIANCA on two different MRI protocols and populations. WMH volumes derived with BIANCA showed good correlations with visual ratings and age. BIANCA is promising for application in large cross-sectional cohort studies.
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影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
DOI：
10.1136/bmj.c3666
发表时间：
2010-07-26
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Debette S;Markus HS
通讯作者：
Markus HS
影响因子：
8.3
作者：
Jeerakathil, T;Wolf, PA;DeCarli, C
通讯作者：
DeCarli, C
影响因子：
29
作者：
Gupta, Mohit;King, Kevin S.;Peshock, Ronald M.
通讯作者：
Peshock, Ronald M.
影响因子：
5.7
作者：
Admiraal-Behloul, F;van den Heuvel, DMJ;Relber, JHC
通讯作者：
Relber, JHC