期刊
NEUROIMAGE
卷 141, 期 -, 页码 191-205出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2016.07.018
关键词
White matter hyperintensities; Automated segmentation; Brain MRI; Neurodegeneration; Vascular pathology
资金
- Wellcome Trust
- Wolfson Foundation
- UK Stroke Association
- NIHR Oxford Biomedical Research Centre
- NIHR Senior Investigator Award
- Wellcome Trust Senior Investigator Award
- China Scholarship Council
- Engineering and Physical Sciences Research Council [1652636] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0514-10168] Funding Source: researchfish
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. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据