Automatic Control and Computer Sciences

, Volume 52, Issue 5, pp 439–450 | Cite as

3D Deep Learning for Automatic Brain MR Tumor Segmentation with T-Spline Intensity Inhomogeneity Correction

  • G. Anand KumarEmail author
  • P. V. SrideviEmail author


Automatic segmentation of brain tumor data is a herculean task for medical applications, particularly in cancer diagnosis. This paper emulates some challenging issues such as noise sensitivity, partial volume averaging, intensity inhomogeneity, inter-slice intensity variations, and intensity non-standardization. This paper intends a novel N3T-spline intensity inhomogeneity correction for bias field correction and the three dimension convolutional neural network (3DCNN) for automatic segmentation. The proposed work consists of four stages (i) pre-processing, (ii) feature extraction (iii) automatic segmentation and (iv) post-processing. In the pre-processing step, novel nonparametric non-uniformity normalization (N3) based T-spline approach is proposed to correct the bias field distortion, which recedes the noises and intensity variations. The extended gray level co-occurrence matrix (EGLCM) is a feature extraction technique, from which the texture patches more suitable for brain tumor segmentation can be extracted. The proposed 3DCNN automatically segments the brain tumor and divides the discrete abnormal tissues from the raw data and EGLCM features. Finally, a simple threshold scheme is adapted on the segmented result to correct the false labels and eliminate the 3D connected small regions. The simulation results in the proposed segmentation procedure could acquire competitive performance as compared with the existing procedure for the BRATS 2015 dataset.


magnetic resonance imaging segmentation brain tumor deep learning convolutional neural network 


  1. 1.
    Balafar, M.A., Ramli, A.R., Saripan, M.I., and Mashohor, S., Review of brain MRI image segmentation methods, Artif. Intell. Rev., 2010, vol. 33, no. 3, pp. 261–274.CrossRefGoogle Scholar
  2. 2.
    Khotanlou, H., Colliot, O., Atif, J., and Bloch, I., 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models, Fuzzy Sets Syst., 2009, vol. 160, no. 10, pp. 1457–1473.MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ahmed, N.M., Yamany, S.M., Mohamed, N., Farag, A.A., and Moriarty, T., A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imaging, 2002, vol. 21, no. 3, pp. 193–199.CrossRefGoogle Scholar
  4. 4.
    Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., and Zhu, Y., Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation, Comput. Vision Image Understanding, 2011, vol. 115, no. 2, pp. 256–269.CrossRefGoogle Scholar
  5. 5.
    Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., and Bezdek, J.C., A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Trans. Neural Networks, 1992, vol. 3, no. 5, pp. 672–682.CrossRefGoogle Scholar
  6. 6.
    Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al., The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE Trans. Med. Imaging, 2015, vol. 34, no. 10, pp. 1993–2024.CrossRefGoogle Scholar
  7. 7.
    Shen, S., Sandham, W., Granat, M., and Sterr, A., MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization, IEEE Trans. Inf. Technol. Biomed., 2005, vol. 9, no. 3, pp. 459–467.CrossRefGoogle Scholar
  8. 8.
    Dou, W., Ruan, S., Chen, Y., Bloyet, D., and Constans, J.M., A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images, Image Vision Comput., 2007, vol. 25, no. 2, pp. 164–171.CrossRefGoogle Scholar
  9. 9.
    Ho, S., Bullitt, E., and Gerig, G., Level-set evolution with region competition: Automatic 3-D segmentation of brain tumors, Pattern Recognition, 2002. Proceedings. 16th International Conference, 2002, vol. 1, pp. 532–535.Google Scholar
  10. 10.
    Gordillo, N., Montseny, E., and Sobrevilla, P., State of the art survey on MRI brain tumor segmentation, Magn. Reson. Imaging, 2013, vol. 31, no. 8, pp. 1426–1438.CrossRefGoogle Scholar
  11. 11.
    Prastawa, M., Bullitt, E., Ho, S., and Gerig, G., A brain tumor segmentation framework based on outlier detection, Med. Image Anal., 2004, vol. 8, no. 3, pp. 275–283.CrossRefGoogle Scholar
  12. 12.
    Kapur, T., Grimson, W.E., Wells, W.M., and Kikinis, R., Segmentation of brain tissue from magnetic resonance images, Med. Image Anal., 1996, vol. 1, no. 2, pp. 109–127.CrossRefGoogle Scholar
  13. 13.
    Fletcher-Heath, L.M., Hall, L.O., Goldgof, D.B., and Murtagh, F.R., Automatic segmentation of non-enhancing brain tumors in magnetic resonance images, Artif. Intell. Med., 2001, vol. 21, no. 1, pp. 43–63.CrossRefGoogle Scholar
  14. 14.
    Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., and Gerig, G., Automatic brain tumor segmentation by subject specific modification of atlas priors, Acad. Radiol., 2003, vol. 10, no. 12, pp. 1341–1348.CrossRefGoogle Scholar
  15. 15.
    Mazzara, G.P., Velthuizen, R.P., Pearlman, J.L., Greenberg, H.M., and Wagner, H., Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation, Int. J. Radiat. Oncol. Biol. Phys., 2004, vol. 59, no. 1, pp. 300–312.CrossRefGoogle Scholar
  16. 16.
    Pereira, S., Pinto, A., Alves, V., and Silva, C.A., Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imaging, 2016, vol. 35, no. 5, pp. 1240–1251.CrossRefGoogle Scholar
  17. 17.
    Xie, K., Yang, J., Zhang, Z.G., and Zhu, Y.M., Semi-automated brain tumor and edema segmentation using MRI, Eur. J. Radiol., 2005, vol. 56, no. 1, pp. 12–19.CrossRefGoogle Scholar
  18. 18.
    Zou, K.H., Warfield, S.K., Bharatha, A., Tempany, C.M., Kaus, M.R., Haker, S.J., Wells, W.M., Jolesz, F.A., and Kikinis, R., Statistical validation of image segmentation quality based on a spatial overlap index 1: Scientific reports, Acad. Radiol., 2004, vol. 11, no. 2, pp. 178–189.CrossRefGoogle Scholar
  19. 19.
    Kaus, M., Warfield, S.K., Jolesz, F.A., and Kikinis, R., Adaptive template moderated brain tumor segmentation in MRI, in Bildverarbeitung für die Medizin, 1999, pp. 102–106.Google Scholar
  20. 20.
    Clarke, L.P., Velthuizen, R.P., Clark, M., Gaviria, J., Hall, L., Goldgof, D., Murtagh, R., Phuphanich, S., and Brem, S., MRI measurement of brain tumor response: Comparison of visual metric and automatic segmentation, Magn. Reson. Imaging, 1998, vol. 16, no. 3, pp. 271–279.CrossRefGoogle Scholar
  21. 21.
    Phillips, W.E., Velthuizen, R.P., Phuphanich, S., Hall, L.O., Clarke, L.P., and Silbiger, M.L., Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme, Magn. Reson. Imaging, 1995, vol. 13, no. 2, pp. 277–290.CrossRefGoogle Scholar
  22. 22.
    Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., and Cuadra, M.B., A review of atlas-based segmentation for magnetic resonance brain images, Comput. Methods Programs Biomed., 2011, vol. 104, no. 3, pp. 158–177.CrossRefGoogle Scholar
  23. 23.
    Prastawa, M., Bullitt, E., and Gerig, G., Simulation of brain tumors in MR images for evaluation of segmentation efficacy, Med. Image Anal., 2009, vol. 13, no. 2, pp. 297–311.CrossRefGoogle Scholar
  24. 24.
    Ahmed, S., Iftekharuddin, K.M., and Vossough, A., Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI, IEEE Trans. Inf. Technol. Biomed., 2011, vol. 15, no. 2, pp. 206–213.CrossRefGoogle Scholar
  25. 25.
    Vaidyanathan, M., Clarke, L.P., Hall, L.O., Heidtman, C., Velthuizen, R., Gosche, K., Phuphanich, S., Wagner, H., Greenberg, H., and Silbiger, M.L., Monitoring brain tumor response to therapy using MRI segmentation, Magn. Reson. Imaging, 1997, vol. 15, no. 3, pp. 323–334.CrossRefGoogle Scholar
  26. 26.
    Kwon, D., Shinohara, R.T., Akbari, H., and Davatzikos, C., Combining generative models for multifocal glioma segmentation and registration, Medical Image Computing and Comput.-Assisted Intervention-MICCAI 2014, New York, 2014, pp. 763–770.Google Scholar
  27. 27.
    Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., and Lanczi, L., The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imaging, 2015, vol. 34, no. 10, pp. 1993–2024.CrossRefGoogle Scholar
  28. 28.
    Urban, G., Bendszus, M., Hamprecht, F., and Kleesiek, J., Multi-modal brain tumor segmentation using deep convolutional neural networks, MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), 2014, pp. 1–15.Google Scholar
  29. 29.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., and Larochelle, H., Brain tumor segmentation with deep neural net-works 2015., ArXiv:1505.03540v.Google Scholar
  30. 30.
    Ma, C., Luo, G., and Wang, K., Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images, IEEE Trans. Med. Imaging, 2018, vol. 37, no. 8, pp. 1943–1954.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous)VisakhapatnamIndia
  2. 2.Department of Electronics and Communication Engineering, Andhra University College of Engineering (Autonomous)VisakhapatnamIndia

Personalised recommendations