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Neuroimage. 2016 Nov 1;141:191-205. doi: 10.1016/j.neuroimage.2016.07.018. Epub 2016 Jul 9.

BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

Author information

1
Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK. Electronic address: ludovica.griffanti@ndcn.ox.ac.uk.
2
Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
3
Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
4
Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
5
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.

Abstract

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.

KEYWORDS:

Automated segmentation; Brain MRI; Neurodegeneration; Vascular pathology; White matter hyperintensities

PMID:
27402600
PMCID:
PMC5035138
DOI:
10.1016/j.neuroimage.2016.07.018
[Indexed for MEDLINE]
Free PMC Article

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