Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion

  • Mengjin DongEmail author
  • Ipek Oguz
  • Nagesh Subbana
  • Peter Calabresi
  • Russell T. Shinohara
  • Paul Yushkevich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)


This paper adapts the joint label fusion (JLF) multi-atlas image segmentation algorithm to the problem of multiple sclerosis (MS) lesion segmentation in multi-modal MRI. Conventionally, JLF requires a set of atlas images to be co-registered to the target image using deformable registration. However, given the variable spatial distribution of lesions in the brain, whole-brain deformable registration is unlikely to line up lesions between atlases and the target image. As a solution, we propose to first pre-segment the target image using an intensity regression based technique, yielding a set of “candidate” lesions. Each “candidate” lesion is then matched to a set of similar lesions in the atlas based on location and size; and deformable registration and JLF are applied at the level of the “candidate” lesion. The approach is evaluated on a dataset of 74 subjects with MS and shown to improve Dice similarity coefficient with reference manual segmentation by 12% over intensity regression technique.



This work was supported, in part, by the NIH grants NIBIB R01EB017255, NINDS R01NS082347, NINDS R01NS085211, NINDS R21NS093349, NINDS R01NS094456.


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  3. 3.
    Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23(Suppl. 1), S139–S150 (2004)CrossRefGoogle Scholar
  4. 4.
    Freifeld, O., Greenspan, H., Goldberger, J.: Multiple sclerosis lesion detection using constrained GMM and curve evolution. Int. J. Biomed. Imaging 2009, 715124 (2009)CrossRefGoogle Scholar
  5. 5.
    Guizard, N., Coupé, P., Fonov, V.S., Manjón, J.V., Arnold, D.L., Collins, D.L.: Rotation-invariant multi-contrast non-local means for MS lesion segmentation. Neuroimage Clin. 8, 376–389 (2015)CrossRefGoogle Scholar
  6. 6.
    Harmouche, R., Subbanna, N.K., Collins, D.L., Arnold, D.L., Arbel, T.: Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighborhood information. IEEE Trans. Biomed. Eng. 62(5), 1281–1292 (2015)CrossRefGoogle Scholar
  7. 7.
    Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–219 (2015)CrossRefGoogle Scholar
  8. 8.
    Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J.C., Quiles, A., Valls, L., Ramió-Torrentà, L., Rovira, A.: Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf. Sci. 186(1), 164–185 (2012)CrossRefGoogle Scholar
  9. 9.
    Mechrez, R., Goldberger, J., Greenspan, H.: Patch-based segmentation with spatial consistency: application to ms lesions in brain MRI. J. Biomed. Imaging 2016, 3 (2016)Google Scholar
  10. 10.
    Roy, S., He, Q., Carass, A., Jog, A., Cuzzocreo, J.L., Reich, D.S., Prince, J., Pham, D.: Example based lesion segmentation. In: SPIE Medical Imaging, p. 90341Y. International Society for Optics and Photonics (2014)Google Scholar
  11. 11.
    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.: An automated tool for detection of flair-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4), 3774–3783 (2012)CrossRefGoogle Scholar
  12. 12.
    Shiee, N., Bazin, P.L., Ozturk, A., Reich, D.S., Calabresi, P.A., Pham, D.L.: A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49(2), 1524–1535 (2010)CrossRefGoogle Scholar
  13. 13.
    Sweeney, E.M., Shinohara, R.T., Shiee, N., Mateen, F.J., Chudgar, A.A., Cuzzocreo, J.L., Calabresi, P.A., Pham, D.L., Reich, D.S., Crainiceanu, C.M.: OASIS is automated statistical inference for segmentation, with applications to multiple sclerosis lesion segmentation in MRI. NeuroImage: Clin. 2, 402–413 (2013)CrossRefGoogle Scholar
  14. 14.
    Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)CrossRefGoogle Scholar
  15. 15.
    Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 611–623 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mengjin Dong
    • 1
    Email author
  • Ipek Oguz
    • 1
  • Nagesh Subbana
    • 1
  • Peter Calabresi
    • 2
  • Russell T. Shinohara
    • 3
  • Paul Yushkevich
    • 1
  1. 1.Penn Image Computing and Science LabUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.The Johns Hopkins Calabresi LabJohns Hopkins UniversityBaltimoreUSA
  3. 3.Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaUSA

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