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Brain Tumour Segmentation Using Convolution Neural Network

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1567))

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Abstract

Automatic brain tumor segmentation is an ill-posed problem. In this paper, we propose convolution neural network based approach for brain tumor segmentation. The proposed network made up of encoder-decoder modules. The encoder modules are designed to encode the input brain MRI slice into set of features while the decoder modules for the generation of the brain tumor segmentation map from the encoded features. To maintain the structural consistency, feature maps obtained on the encoder side are shared with the respective decoder modules using skip connections. We have used training set of the BraTS-15 dataset to train the proposed network for brain tumor segmentation. While, its testing set is used to validate the proposed network for brain tumor segmentation. The experimental analysis consists the comparison of the proposed and existing methods for brain tumor segmentation with the help of Dice similarity coefficient and Jaccard index. Comparison with the existing methods show that the proposed method outperforms other existing methods for brain tumor segmentation.

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References

  1. Alex, V., Safwan, M., Krishnamurthi, G.: Brain tumor segmentation from multi modal MR images using fully convolutional neural network. In: Medical Image Computing and Computer Assisted Intervention-MICCAI, pp. 1–8 (2017)

    Google Scholar 

  2. Ayachi, R., Ben Amor, N.: Brain tumor segmentation using support vector machines. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS (LNAI), vol. 5590, pp. 736–747. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02906-6_63

    Chapter  Google Scholar 

  3. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  4. Cui, S., Mao, L., Jiang, J., Liu, C., Xiong, S.: Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J. Healthc. Eng. 2018 (2018)

    Google Scholar 

  5. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44

    Chapter  Google Scholar 

  6. Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 179–187. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_19

    Chapter  Google Scholar 

  7. Dudhane, A., Biradar, K.M., Patil, P.W., Hambarde, P., Murala, S.: Varicolored image de-hazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4564–4573 (2020)

    Google Scholar 

  8. Dudhane, A., Hambarde, P., Patil, P., Murala, S.: Deep underwater image restoration and beyond. IEEE Sig. Process. Lett. 27, 675–679 (2020)

    Article  Google Scholar 

  9. Dudhane, A., Murala, S.: C\(\hat{}\) 2msnet: a novel approach for single image haze removal. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1397–1404. IEEE (2018)

    Google Scholar 

  10. Dudhane, A., Murala, S.: CDNet: single image de-hazing using unpaired adversarial training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1147–1155. IEEE (2019)

    Google Scholar 

  11. Dudhane, A., Singh Aulakh, H., Murala, S.: Ri-GAN: an end-to-end network for single image haze removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  12. Hambarde, P., Dudhane, A., Murala, S.: Single image depth estimation using deep adversarial training. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 989–993. IEEE (2019)

    Google Scholar 

  13. Hambarde, P., Dudhane, A., Patil, P.W., Murala, S., Dhall, A.: Depth estimation from single image and semantic prior. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1441–1445. IEEE (2020)

    Google Scholar 

  14. Hambarde, P., Murala, S.: S2DNet: depth estimation from single image and sparse samples. IEEE Trans. Comput. Imaging 6, 806–817 (2020)

    Article  Google Scholar 

  15. Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  17. Hussain, S., Anwar, S.M., Majid, M.: Brain tumor segmentation using cascaded deep convolutional neural network. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1998–2001. IEEE (2017)

    Google Scholar 

  18. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976. IEEE (2017)

    Google Scholar 

  19. Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  20. Kolekar, P., Kendule, J.: Brain tumour segmentation using convolution neural network. J. Xian Univ. Arch. Technol. (2018)

    Google Scholar 

  21. Kong, X., Sun, G., Wu, Q., Liu, J., Lin, F.: Hybrid pyramid U-net model for brain tumor segmentation. In: Shi, Z., Mercier-Laurent, E., Li, J. (eds.) IIP 2018. IAICT, vol. 538, pp. 346–355. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00828-4_35

    Chapter  Google Scholar 

  22. Li, Z., Wang, Y., Yu, J., Guo, Y., Cao, W.: Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci. Rep. 7(1), 5467 (2017)

    Article  Google Scholar 

  23. Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)

    Google Scholar 

  24. Lin, G.C., Wang, W.J., Wang, C.M., Sun, S.Y.: Automated classification of multi-spectral MR images using linear discriminant analysis. Comput. Med. Imaging Graph. 34(4), 251–268 (2010)

    Article  Google Scholar 

  25. Liu, J., et al.: A cascaded deep convolutional neural network for joint segmentation and genotype prediction of brainstem gliomas. IEEE Trans. Biomed. Eng. (2018)

    Google Scholar 

  26. Mehta, N., Murala, S.: MSAR-Net: multi-scale attention based light-weight image super-resolution. Pattern Recogn. Lett. 151, 215–221 (2021)

    Article  Google Scholar 

  27. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BraTs). IEEE Trans. Med. Imaging 34(10), 1993 (2015)

    Article  Google Scholar 

  28. Nema, S., Dudhane, A., Murala, S., Naidu, S.: RescueNet: an unpaired GAN for brain tumor segmentation. Biomed. Sig. Process. Control 55, 101641 (2020)

    Article  Google Scholar 

  29. Nie, D., et al.: Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. (2018)

    Google Scholar 

  30. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520–1528 (2015)

    Google Scholar 

  31. Patil, P., Murala, S.: FGGAN: a cascaded unpaired learning for background estimation and foreground segmentation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1770–1778. IEEE (2019)

    Google Scholar 

  32. Patil, P.W., Biradar, K.M., Dudhane, A., Murala, S.: An end-to-end edge aggregation network for moving object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8149–8158 (2020)

    Google Scholar 

  33. Patil, P.W., Dudhane, A., Chaudhary, S., Murala, S.: Multi-frame based adversarial learning approach for video surveillance. Pattern Recogn. 122, 108350 (2022)

    Article  Google Scholar 

  34. Patil, P.W., Dudhane, A., Kulkarni, A., Murala, S., Gonde, A.B., Gupta, S.: An unified recurrent video object segmentation framework for various surveillance environments. IEEE Trans. Image Process. 30, 7889–7902 (2021)

    Article  Google Scholar 

  35. Patil, P.W., Thawakar, O., Dudhane, A., Murala, S.: Motion saliency based generative adversarial network for underwater moving object segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1565–1569. IEEE (2019)

    Google Scholar 

  36. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)

    Article  Google Scholar 

  37. Phutke, S.S., Murala, S.: Diverse receptive field based adversarial concurrent encoder network for image inpainting. IEEE Sig. Process. Lett. 28, 1873–1877 (2021)

    Article  Google Scholar 

  38. Rezaei, M., et al.: A conditional adversarial network for semantic segmentation of brain tumor. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 241–252. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_21

    Chapter  Google Scholar 

  39. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  40. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  41. Smoll, N.R., Schaller, K., Gautschi, O.P.: Long-term survival of patients with glioblastoma multiforme (GBM). J. Clin. Neurosci. 20(5), 670–675 (2013)

    Article  Google Scholar 

  42. Thawakar, O., Patil, P.W., Dudhane, A., Murala, S., Kulkarni, U.: Image and video super resolution using recurrent generative adversarial network. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2019)

    Google Scholar 

  43. Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_16

    Chapter  Google Scholar 

  44. Wu, Z., Paulsen, K.D., Sullivan, J.M.: Adaptive model initialization and deformation for automatic segmentation of t1-weighted brain MRI data. IEEE Trans. Biomed. Eng. 52(6), 1128–1131 (2005)

    Article  Google Scholar 

  45. Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.: SeGAN: adversarial network with multi-scale l 1 loss for medical image segmentation. Neuroinformatics, 1–10 (2018)

    Google Scholar 

  46. Yi, D., Zhou, M., Chen, Z., Gevaert, O.: 3-D convolutional neural networks for glioblastoma segmentation. arXiv preprint arXiv:1611.04534 (2016)

  47. Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, VJTI, Mumbai, India

    Karuna Bhalerao, Shital Patil & Surendra Bhosale

  2. Department of Instrumentation Engineering, RAIT, Navi Mumbai, India

    Shital Patil

Authors
  1. Karuna Bhalerao
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  2. Shital Patil
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  3. Surendra Bhosale
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Corresponding author

Correspondence to Surendra Bhosale .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Bhalerao, K., Patil, S., Bhosale, S. (2022). Brain Tumour Segmentation Using Convolution Neural Network. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-11346-8_17

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  • Online ISBN: 978-3-031-11346-8

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