, Volume 16, Issue 2, pp 269–281 | Cite as

Validation and Optimization of BIANCA for the Segmentation of Extensive White Matter Hyperintensities

  • Yifeng Ling
  • Eric Jouvent
  • Louis Cousyn
  • Hugues Chabriat
  • François De Guio
Original Article


White matter hyperintensities (WMH) are a hallmark of small vessel diseases (SVD). Yet, no automated segmentation method is readily and widely used, especially in patients with extensive WMH where lesions are close to the cerebral cortex. BIANCA (Brain Intensity AbNormality Classification Algorithm) is a new fully automated, supervised method for WMH segmentation. In this study, we optimized and compared BIANCA against a reference method with manual editing in a cohort of patients with extensive WMH. This was achieved in two datasets: a clinical protocol with 90 patients having 2-dimensional FLAIR and an advanced protocol with 66 patients having 3-dimensional FLAIR. We first determined simultaneously which input modalities (FLAIR alone or FLAIR + T1) and which training sets were better compared to the reference. Three strategies for the selection of the threshold that is applied to the probabilistic output of BIANCA were then evaluated: chosen at the group level, based on Fazekas score or determined individually. Accuracy of the segmentation was assessed through measures of spatial agreement and volumetric correspondence with respect to reference segmentation. Based on all our tests, we identified multimodal inputs (FLAIR + T1), mixed WMH load training set and individual threshold selection as the best conditions to automatically segment WMH in our cohort. A median Dice similarity index of 0.80 (0.80) and an intraclass correlation coefficient of 0.97 (0.98) were obtained for the clinical (advanced) protocol. However, Bland-Altman plots identified a difference with the reference method that was linearly related to the total burden of WMH. Our results suggest that BIANCA is a reliable and fast segmentation method to extract masks of WMH in patients with extensive lesions.


Automated segmentation White matter hyperintensities BIANCA Brain MRI Small vessel disease CADASIL 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University Paris Diderot, Sorbonne Paris Cité, UMR-S 1161 INSERMParisFrance
  2. 2.Huashan Hospital, Department of NeurologyFudan UniversityShanghaiChina
  3. 3.Department of NeurologyAP-HP, Lariboisière HospitalParisFrance
  4. 4.DHU NeuroVasc Sorbonne Paris CitéParisFrance

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