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A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI

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Abstract

This paper presents a novel approach for automatic segmentation of MS lesion including both of number and volume. The novelty includes the combination of the multiplicative intrinsic component optimization algorithm (Li et al. in Magn Reson Imaging 32:913–923, 2014) in bias field correction and normal tissue segmentation simultaneously, and the development of a multi-atlas and multi-channel (MAMC) segmentation approach. The first research focus is the classification of brain tissue into white matter, cerebrospinal fluid and gray matter in T1-w image and FLAIR image. The second research focus is the segmentation of MS lesion in white matter region using atlas. In label fusion, the coefficient as a specific weight is assigned to target label image based on the correlation function between atlases. This novel MAMC approach is evaluated by 20 training cases obtained from Medical Image Computing and Computer Aided Intervention Society 2008 MS Lesions Segmentation Challenge. The numerical results are presented in terms of accuracy, specificity and absolute volume difference. A comparison of MAMC approach and other conventional approaches is presented in terms of the true positive rate and the positive predictive value. Furthermore, the total lesion volume is calculated and compared with expert delineation. It can be seen that the MAMC approach is able to acquire a larger mean value of the Dice similarity coefficient than the other conventional approaches do. Therefore, this novel approach is an added value for the clinical evaluation of MS patients.

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References

  1. Li, C., Gore, J.C., Davatzikos, C.: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging 32(7), 913–923 (2014)

    Article  Google Scholar 

  2. Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., et al.: Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf. Sci. 186(1), 164–185 (2012)

    Article  Google Scholar 

  3. Gaser, C., Schmidt, P., Arsic, M., et al.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage 59(4), 3774–3783 (2012)

    Article  Google Scholar 

  4. Tohka, J.: Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: a review. World J. Radiol. 6(11), 855–864 (2014)

    Article  Google Scholar 

  5. Cabezas, M., Oliver, A., Valverde, S., Beltran, B., et al.: BOOST: a supervised approach for multiple sclerosis lesion segmentation. J. Neurosci. Methods 237, 108–117 (2014)

    Article  Google Scholar 

  6. Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26, 405–421 (2007)

    Article  Google Scholar 

  7. Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain MRI image segmentation methods. Artif. Intell. Rev. 33(3), 261–274 (2010)

    Article  Google Scholar 

  8. Pouwels, P.J.W., Steenwijk, M.D., Daams, M., et al.: Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). NeuroImage Clin. 3, 462–469 (2013)

    Article  Google Scholar 

  9. Cerasa, A., Bilotta, E., Augimeri, A., Cherubini, A., et al.: A cellular neural network methodology for the automated segmentation of multiple sclerosis lesions. J. Neurosci. Methods 203(1), 193–199 (2012)

    Article  Google Scholar 

  10. Hadrich, A., Zribi, M., Masmoudi, A.: Bayesian expectation maximization algorithm by using B-splines functions: Application in image segmentation. Math. Comput. Simul. 120(Supplement C), 50–63 (2016)

    Article  MathSciNet  Google Scholar 

  11. Svensson, C.M., Bondoc, K.G., Pohnert, G., Figge, M.T.: Segmentation of clusters by template rotation expectation maximization. Comput. Vis. Image Underst. 154(Supplement C), 64–72 (2017)

    Article  Google Scholar 

  12. Mitra, J., Bourgeat, P., Fripp, J., Ghose, S., et al.: Lesion segmentation from multimodal MRI using random forest following ischemic stroke. NeuroImage 98, 324–335 (2014)

    Article  Google Scholar 

  13. Clatz, O., Geremia, E., Menze, B.H., et al.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2), 378–390 (2011)

    Article  Google Scholar 

  14. Roy, P.K., Bhuiyan, A., Janke, A., Desmond, P.M., et al.: Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov random field. Comput. Med. Imaging Graph. 45, 102–111 (2015)

    Article  Google Scholar 

  15. Bazin, P., Shiee, N., Pham, D.L.: Multiple sclerosis lesion segmentation using statistical and topological atlases. In: Grand Challenge Work: Mult. Scler. Lesion Segm. Challenge, pp. 1–10 (2008)

  16. Freire, P.G.L., Ferrari, R.J.: Automatic iterative segmentation of multiple sclerosis lesions using Student’s t mixture models and probabilistic anatomical atlases in FLAIR images. Comput. Biol. Med. 73(C), 10–23 (2016)

    Article  Google Scholar 

  17. Zhao, Y., Guo, S., Luo, M., Liu, Y., et al.: An energy minimization method for MS lesion segmentation from T1-w and FLAIR images. Magn. Reson. Imaging 39, 1–16 (2017)

    Article  Google Scholar 

  18. García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D.L., et al.: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal. 17(1), 1–18 (2016)

    Article  Google Scholar 

  19. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans. Med. Imaging 28(8), 1266–1277 (2009)

    Article  Google Scholar 

  20. Sabuncu, M.R., Yeo, B.T.T., Van Leemput, K., Fischl, B., et al.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imaging 29(10), 1714–1729 (2010)

    Article  Google Scholar 

  21. Isgum, I., Staring, M., Rutten, A., Prokop, M., et al.: Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28(7), 1000–1010 (2009)

    Article  Google Scholar 

  22. Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., et al.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 611–623 (2013)

    Article  Google Scholar 

  23. Souplet, J.C., Lebrun, C., Ayache, N., Malandain, G.: An automatic segmentation of T2FLAIR multiple sclerosis lesions. In: MICCAI-Multiple Sclerosis Lesion Segmentation Challenge Workshop (2008)

  24. Weiss, N., Rueckert, D., Rao, A.: Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. In: Mori K., et al. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013: 16th International Conference, Nagoya, 22–26 Sept 2013, Proceedings, Part I, pp. 735–742. Springer, Berlin (2013)

  25. Jain, S., Sima, D.M., Ribbens, A., Cambron, M., et al.: Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. NeuroImage Clin. 8, 367–375 (2015)

    Article  Google Scholar 

  26. Doshi, J., Erus, G., Yangming, O., Gaonkar, B., et al.: Multi-atlas skull-stripping. Acad. Radiol. 20(12), 1566–1576 (2013)

    Article  Google Scholar 

  27. Pechaud, M., Jenkinson, M., Smith, S.: BET2: MR-based estimation of brain, skull and scalp surfaces. In: Eleventh Annual Meeting of the Organization for Human Brain Mapping (2005)

  28. http://miccai2008.rutgers.edu/

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Acknowledgements

This work was jointly supported by the Shandong University Science and technology project (No. J17KA082), the National Natural Science Foundation of China (No. 61401259), the China Postdoctoral Science Foundation (No. 2015M582128) and Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University.

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  1. School of Physics and Electronics, Shandong Normal University, Jinan, China

    Jingjing Wang, Changjun Hu, Huaqiang Xu, Yan Leng, Liren Zhang & Yuefeng Zhao

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  1. Jingjing Wang
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  2. Changjun Hu
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  3. Huaqiang Xu
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  4. Yan Leng
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  5. Liren Zhang
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  6. Yuefeng Zhao
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Correspondence to Jingjing Wang.

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Wang, J., Hu, C., Xu, H. et al. A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI. SIViP 13, 1019–1027 (2019). https://doi.org/10.1007/s11760-019-01440-5

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  • DOI: https://doi.org/10.1007/s11760-019-01440-5

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