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Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI

  • Francesca GalassiEmail author
  • Olivier Commowick
  • Emmanuel Vallee
  • Christian Barillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

We introduce a new framework for the automated and unsupervised segmentation of Multiple Sclerosis lesions from multimodal Magnetic Resonance images. It relies on a voxel-wise approach to detect local white matter abnormalities, with an a-contrario analysis, which takes into account local information. First, a voxel-wise comparison of multimodal patient images to a set of controls is performed. Then, region-based probabilities are estimated using an a-contrario approach. Finally, correction for multiple testing is performed. Validation was undertaken on a multi-site clinical dataset of 53 MS patients with various number and volume of lesions. We showed that the proposed framework outperforms the widely used FDR-correction for this type of analysis, particularly for low lesion loads.

Keywords

Multiple Sclerosis Voxel-wise comparison a-contrario 

References

  1. 1.
    Garcia-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D.L., Collins, D.L.: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal. 17(1), 1–18 (2013)CrossRefGoogle Scholar
  2. 2.
    Polman, C.H., et al.: Diagnostic criteria for multiple sclerosis: 2005 revisions to the Mc Donald criteria. Ann. Neurol. 58, 840–846 (2005)CrossRefGoogle Scholar
  3. 3.
    Robin, A., Moisan, L., Le Hgarat-Mascle, S.: An a-contrario approach for sub-pixel change detection in satellite imagery. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1977–93 (2010)CrossRefGoogle Scholar
  4. 4.
    Maumet, C., Maurel, P., Ferré, J.C., Barillot, C.: An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling. NeuroImage 134, 424–433 (2016)CrossRefGoogle Scholar
  5. 5.
    Rousseau, F., et al.: An a contrario approach for change detection in 3D multimodal images: application to multiple sclerosis in MRI. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 2069–2072 (2007)Google Scholar
  6. 6.
    Crimi, A., Commowick, O., Ferr, J.C., Maarouf, A., Edan, G., Barillot, C.: Semi-automatic classification of lesion patterns in patients with clinically isolated syndrome. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1102–1105 (2013)Google Scholar
  7. 7.
    Commowick, O., Maarouf, A., Ferré, J.C., Ranjeva, J.P., Edan, G., Barillot, C.: Diffusion MRI abnormalities detection with orientation distribution functions: a multiple sclerosis longitudinal study. Med. Image Anal. 22, 114–23 (2015)CrossRefGoogle Scholar
  8. 8.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  9. 9.
    Gabr, R.E., Hasan, K.M., Haque, M.E., Nelson, F.M., Wolinsky, J.S., Narayana, P.A.: Optimal combination of FLAIR and T2 weighted MRI for improved lesion contrast in multiple sclerosis. J. Magn. Reson. Imaging 44, 1293–1300 (2016)CrossRefGoogle Scholar
  10. 10.
    Commowick, O., et al.: Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. bioRxiv 367557 (2018)Google Scholar
  11. 11.
    Akhondi-Asl, A., Hoyte, L., Lockhart, M.E., Warfield, S.K.: A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights. IEEE Trans. Med. Imaging 33, 1997–2009 (2014)CrossRefGoogle Scholar
  12. 12.
    Coupe, P., et al.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27, 425–41 (2008)CrossRefGoogle Scholar
  13. 13.
    Commowick, O., Wiest-Daesslé, N., Prima, S.: Block matching strategies for rigid registration of multimodal medical images. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 700–703 (2012)Google Scholar
  14. 14.
    Manjn, J.V., Coup, P.: volBrain: an online MRI brain volumetry system. Front. Neuroinformatics 10, 30 (2016)Google Scholar
  15. 15.
    Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)CrossRefGoogle Scholar
  16. 16.
    Guimond, A., Meunier, J., Thirion, J.P.: Average brain models. Comput. Vis. Image Underst. 77, 192–210 (2000)CrossRefGoogle Scholar
  17. 17.
    Commowick, O., Wiest-Daesslé, N., Prima, S.: Automated diffeomorphic registration of anatomical structures with rigid parts: application to dynamic cervical MRI. MICCAI 15, 163–170 (2012)Google Scholar
  18. 18.
    Virmani, D., Taneja, S., Malhotra, G.: Normalization based K means Clustering Algorithm. arXiv:1503.00900 (2015)
  19. 19.
    Hochberg, Y., Tamhane, A.: Multiple Comparison Procedures. Wiley, Hoboken (1987)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Francesca Galassi
    • 1
    Email author
  • Olivier Commowick
    • 1
  • Emmanuel Vallee
    • 2
  • Christian Barillot
    • 1
  1. 1.Inria, CNRS, Inserm, IRISA, VisAGeSRennesFrance
  2. 2.FMRIB, NDCNUniversity of OxfordOxfordUK

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