Skip to main content
SpringerLink
Log in

Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network

  • Chapter
  • First Online:
Deep Learning for Cancer Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 908))

Abstract

Gliomas are considered as the most aggressive and commonly found type among brain tumors. This leads to the shortage of lives of oncological patients. These tumors are mostly by magnetic resonance imaging (MRI) from which the segmentation becomes a big problem because of the large structural and spatial variability. In this study, we propose a 2D-UNET model based on convolutional neural networks (CNN). The model is trained, validated and tested on BRATS 2019 dataset. The average dice coefficient achieved is 0.9694.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. G.P. Mazzara, R.P. Velthuizen, J.L. Pearlman, H.M. Greenberg, H. Wagner, Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int. J. Radiat. Oncol. Biol. Phys 59(1), 300–312 (2004)

    Article  Google Scholar 

  2. T. Yamahara, Y. Numa, T. Oishi, T. Kawaguchi, T. Seno, A. Asai, Keiji Kawamoto, Morphological and flow cytometric analysis of cell infiltration in glioblastoma: a comparison of autopsy brain and neuroimaging. Brain Tumor Pathol. 27(2), 81–87 (2010)

    Article  Google Scholar 

  3. S. Bauer, R. Wiest, L.-P. Nolte, M. Reyes, A survey of mri-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)

    Article  Google Scholar 

  4. T.L. Jones, T.J. Byrnes, G. Yang, F.A. Howe, B.A. Bell, T.R. Barrick, Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. Neuro-oncology 17(3), 466–476 (2015)

    Google Scholar 

  5. M. Soltaninejad, G. Yang, T. Lambrou, N. Allinson, T.L. Jones, T.R. Barrick, F.A. Howe, X. Ye, Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. Comput. Assist. Radiol. Surg. 12(2), 183–203 (2017)

    Article  Google Scholar 

  6. P.A. Mei, C.C. de Carvalho, S.J. Fraser, L.L. Min, F. Reis, Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps. J. Neurol. Sci. 359(1–2), 78–83 (2015)

    Article  Google Scholar 

  7. J. Juan-Albarracin, E. Fuster-Garcia, J.V. Manjon, M. Robles, F. Aparici, L. Martí-Bonmatí, J.M. Garcia-Gomez, Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 10(5), e0125143 (2015)

    Article  Google Scholar 

  8. A. Rajendran, R. Dhanasekaran, Fuzzy clustering and deformable model for tumor segmentation on mri brain image: a combined approach. Procedia Eng. 30, 327–333 (2012)

    Article  Google Scholar 

  9. M. Jafari, S. Kasaei, Automatic brain tissue detection in mri images using seeded region growing segmentation and neural network classification. Aust. J. Bas. Appl. Sci. 5(8), 1066–1079 (2011)

    Google Scholar 

  10. M. Goetz, C. Weber, J. Bloecher, B. Stieltjes, H.-P. Meinzer, K. Maier-Hein (2014) Extremely randomized trees based brain tumor segmentation, in Proceeding of BRATS challenge-MICCAI, pp. 006–011

    Google Scholar 

  11. N. Subbanna, D. Precup, T. Arbel, Iterative multilevel MRF leveraging context and voxel information for brain tumour segmentation in MRI, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 400–405

    Google Scholar 

  12. T.M. Hsieh, Y.-M. Liu, C.-C. Liao, F. Xiao, I.-J. Chiang, J.-M. Wong, Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing. BMC Med. Inf. Decis. Making 11(1), 54 (2011)

    Article  Google Scholar 

  13. W. Wu, A.Y. Chen, L. Zhao, J.J. Corso, Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int. J. Comput. Assist. Radiol. Surg. 9(2), 241–53 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, H. Larochelle, Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  16. K. Kamnitsas, C. Ledig, V.F. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, B. Glocker, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 1(36), 61–78 (2017)

    Article  Google Scholar 

  17. O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241

    Google Scholar 

  18. B.H. Menze et al., The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imag. 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  19. H. Akbari, A. Sotiras, S. Bakas et al., Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features, in ci Data 4, 170117 (2017). https://doi.org/10.1038/sdata.2017.117

  20. S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, R.T. Shinohara, C. Berger, S.M. Ha, M. Rozycki, M. Prastawa et al., Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

  21. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105

    Google Scholar 

  22. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  23. J. Bernal, K. Kushibar, D.S. Asfaw, S. Valverde, A. Oliver, R. Martí, X. Lladó, Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Med. 95, 64–81 (2019)

    Article  Google Scholar 

  24. D. Ciresan, A. Giusti, L.M. Gambardella, J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images, in Advances in Neural Information Processing Systems (2012), pp. 2843–2851

    Google Scholar 

  25. B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imag. 34(10), 1993–2024 (2014)

    Article  Google Scholar 

  26. E. Shelhamer, J. Long, T. Darrell, Fully convolutional networks for semantic segmentation. IEEE Ann. Hist. Comput. 04, 640–651 (2017)

    Google Scholar 

  27. M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, C. Pal, The importance of skip connections in biomedical image segmentation, Deep Learning and Data Labeling for Medical Applications (Springer, Cham, 2016), pp. 179–187

    Chapter  Google Scholar 

  28. F. Milletari, N. Navab, S.-A. Ahmadi, V-net: fully convolutional neural networks for volumetric medical image segmentation, in 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571

    Google Scholar 

  29. D.P. Kingma, J. Ba, Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Rome, Italy

    Khushboo Munir, Fabrizio Frezza & Antonello Rizzi

Authors
  1. Khushboo Munir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabrizio Frezza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonello Rizzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khushboo Munir .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Faculty of Engineering, Al-Balqa Applied University, Balqa, Jordan

    Jafar Alzubi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Munir, K., Frezza, F., Rizzi, A. (2021). Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_14

Download citation

Publish with us

Policies and ethics

Search

Navigation