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

Abstract

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.

Keywords

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

Notes

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.

References

  1. Auer, D. P., Pütz, B., Gössl, C., Elbel, G.-K., Gasser, T., & Dichgans, M. (2001). Differential lesion patterns in CADASIL and sporadic subcortical arteriosclerotic encephalopathy: MR imaging study with statistical parametric group comparison 1. Radiology, 218(2), 443–451.CrossRefPubMedGoogle Scholar
  2. Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (London, England), 1(8476), 307–310.CrossRefGoogle Scholar
  3. Caligiuri, M. E., Perrotta, P., Augimeri, A., Rocca, F., Quattrone, A., & Cherubini, A. (2015). Automatic detection of white matter Hyperintensities in healthy aging and pathology using magnetic resonance imaging: A review. Neuroinformatics, 13(3), 261–276.  https://doi.org/10.1007/s12021-015-9260-y.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chabriat, H., Joutel, A., Dichgans, M., Tournier-Lasserve, E., & Bousser, M.-G. (2009). Cadasil. The Lancet Neurology, 8(7), 643–653.CrossRefPubMedGoogle Scholar
  5. Chabriat, H., Hervé, D., Duering, M., Godin, O., Jouvent, E., Opherk, C., Alili, N., Reyes, S., Jabouley, A., Zieren, N., Guichard, J. P., Pachai, C., Vicaut, E., & Dichgans, M. (2016). Predictors of clinical worsening in cerebral autosomal dominant Arteriopathy with subcortical infarcts and leukoencephalopathy prospective cohort study. Stroke, 47(1), 4–11.CrossRefPubMedGoogle Scholar
  6. Damangir, S., Manzouri, A., Oppedal, K., Carlsson, S., Firbank, M. J., Sonnesyn, H., Tysnes, O. B., O'Brien, J. T., Beyer, M. K., Westman, E., Aarsland, D., Wahlund, L. O., & Spulber, G. (2012). Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines. Journal of the Neurological Sciences, 322(1–2), 211–216.  https://doi.org/10.1016/j.jns.2012.07.064.CrossRefPubMedGoogle Scholar
  7. Damangir, S., Westman, E., Simmons, A., Vrenken, H., Wahlund, L.-O., & Spulber, G. (2017). Reproducible segmentation of white matter hyperintensities using a new statistical definition. Magma, 30(3), 227–237.  https://doi.org/10.1007/s10334-016-0599-3.CrossRefPubMedGoogle Scholar
  8. De Guio, F., Reyes, S., Duering, M., Pirpamer, L., Chabriat, H., & Jouvent, E. (2014). Decreased T1 contrast between gray matter and normal-appearing white matter in CADASIL. American Journal of Neuroradiology, 35(1), 72–76.  https://doi.org/10.3174/ajnr.A3639.CrossRefPubMedGoogle Scholar
  9. De Guio, F., Jouvent, E., Biessels, G. J., Black, S. E., Brayne, C., Chen, C., et al. (2016). Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 36(8), 1319–1337.  https://doi.org/10.1177/0271678X16647396.CrossRefGoogle Scholar
  10. De Guio, F., Vignaud, A., Chabriat, H., & Jouvent, E. (2017). Different types of white matter hyperintensities in CADASIL: Insights from 7-tesla MRI. Journal of Cerebral Blood Flow and Metabolism, 0271678X1769016.  https://doi.org/10.1177/0271678X17690164.
  11. Fazekas, F., Chawluk, J. B., Alavi, A., Hurtig, H. I., & Zimmerman, R. A. (1987). MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. American Journal of Roentgenology, 149(2), 351–356.CrossRefPubMedGoogle Scholar
  12. Griffanti, L., Zamboni, G., Khan, A., Li, L., Bonifacio, G., Sundaresan, V., Schulz, U. G., Kuker, W., Battaglini, M., Rothwell, P. M., & Jenkinson, M. (2016). BIANCA (brain intensity AbNormality classification algorithm): A new tool for automated segmentation of white matter hyperintensities. NeuroImage, 141, 191–205.  https://doi.org/10.1016/j.neuroimage.2016.07.018.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Grimaud, J., Lai, M., Thorpe, J., Adeleine, P., Wang, L., Barker, G. J., Plummer, D. L., Tofts, P. S., McDonald, W. I., & Miller, D. H. (1996). Quantification of MRI lesion load in multiple sclerosis: A comparison of three computer-assisted techniques. Magnetic Resonance Imaging, 14(5), 495–505.CrossRefPubMedGoogle Scholar
  14. Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143–156.CrossRefPubMedGoogle Scholar
  15. Mangin, J.-F. (Ed.). (2000). IEEE workshop on mathematical methods in biomedical image analysis: Proceedings, Hilton Head Island, South Carolina, June 11–12, 2000. Los Alamitos, Calif: IEEE Computer Society.Google Scholar
  16. Olsson, E., Klasson, N., Berge, J., Eckerström, C., Edman, Å., Malmgren, H., & Wallin, A. (2013). White matter lesion assessment in patients with cognitive impairment and healthy controls: Reliability comparisons between visual rating, a manual, and an automatic Volumetrical MRI method—The Gothenburg MCI study. Journal of Aging Research, 2013, 1–10.  https://doi.org/10.1155/2013/198471.CrossRefGoogle Scholar
  17. Scheltens, P., Barkhof, F., Leys, D., Pruvo, J. P., Nauta, J. J., Vermersch, P., et al. (1993). A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. Journal of the Neurological Sciences, 114(1), 7–12.CrossRefPubMedGoogle Scholar
  18. Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., Hemmer, B., & Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage, 59(4), 3774–3783.  https://doi.org/10.1016/j.neuroimage.2011.11.032.CrossRefPubMedGoogle Scholar
  19. Shiee, N., Bazin, P.-L., Ozturk, A., Reich, D. S., Calabresi, P. A., & Pham, D. L. (2010). A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage, 49(2), 1524–1535.  https://doi.org/10.1016/j.neuroimage.2009.09.005.CrossRefPubMedGoogle Scholar
  20. de Sitter, A., Steenwijk, M. D., Ruet, A., Versteeg, A., Liu, Y., van Schijndel, R. A., et al. (2017). Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study. NeuroImage, 163(Supplement C), 106–114.  https://doi.org/10.1016/j.neuroimage.2017.09.011.CrossRefPubMedGoogle Scholar
  21. Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155.  https://doi.org/10.1002/hbm.10062.CrossRefPubMedGoogle Scholar
  22. Steenwijk, M. D., Pouwels, P. J. W., Daams, M., van Dalen, J. W., Caan, M. W. A., Richard, E., Barkhof, F., & Vrenken, H. (2013). Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). NeuroImage: Clinical, 3, 462–469.  https://doi.org/10.1016/j.nicl.2013.10.003.CrossRefGoogle Scholar
  23. Wardlaw, J. M., Smith, E. E., Biessels, G. J., Cordonnier, C., Fazekas, F., Frayne, R., Lindley, R. I., O'Brien, J. T., Barkhof, F., Benavente, O. R., Black, S. E., Brayne, C., Breteler, M., Chabriat, H., Decarli, C., de Leeuw, F. E., Doubal, F., Duering, M., Fox, N. C., Greenberg, S., Hachinski, V., Kilimann, I., Mok, V., Oostenbrugge Rv, Pantoni, L., Speck, O., Stephan, B. C., Teipel, S., Viswanathan, A., Werring, D., Chen, C., Smith, C., van Buchem, M., Norrving, B., Gorelick, P. B., Dichgans, M., & STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1). (2013). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology, 12(8), 822–838.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© 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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