Research overview - BIANCA (Brain Intensity AbNormality Classification Algorithm)
FMRIB's tool for automated segmentation of white matter hyperintensities
BIANCA is a fully automated, supervised method for white matter hyperintensities (WMH) detection, based on the k-nearest neighbour (k-NN) algorithm. BIANCA classifies the image’s voxels based on their intensity and spatial features, and the output image represents the probability per voxel of being WMH. BIANCA is very flexible in terms of MRI modalities to use and 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 (see user guide for details).
The output from BIANCA will depend critically on the choice of options and the quality of the training data and manual segmentations. Automated tuning methods and pre-trained datasets will hopefully be available in an upcoming release, but for now manual segmentations are necessary.
If you use BIANCA in your research, please make sure that you reference the following article:
[Griffanti 2016] L. Griffanti, G. Zamboni, A. Khan, L. Li, G. Bonifacio, V. Sundaresan, U. G. Schulz, W. Kuker, M. Battaglini, P. M. Rothwell, M. Jenkinson (2016) BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage. 141:191-205.