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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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
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
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)
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)
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)
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)
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)
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
B.H. Menze et al., The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imag. 34(10), 1993–2024 (2015)
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
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)
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
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
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)
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
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)
E. Shelhamer, J. Long, T. Darrell, Fully convolutional networks for semantic segmentation. IEEE Ann. Hist. Comput. 04, 640–651 (2017)
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
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
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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
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
DOI: https://doi.org/10.1007/978-981-15-6321-8_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6320-1
Online ISBN: 978-981-15-6321-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)