Abstract
Brain tumour segmentation poses a challenging task even in the eyes of a trained medical practitioner. Traditional machine learning algorithms require hand-coding features from images before they can learn to identify the regions. Deep learning can solve the problem of detecting tumours with precision and even segment it. Neural networks can learn a hierarchical representation of features from the data by itself. We use a time-distributed architecture for U-Net based deep convolutional neural networks (TD-UNET). We tested our network against the MICCAI BRATS 2015 dataset that comprised 220 high-graded gliomas (HGG) and 54 low-graded gliomas (LGG) and yielded a test case accuracy of 58.3%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dasgupta, A., Gupta, T., Jalali, R.: Indian data on central nervous tumours: a summary of published works. South Asian J. Cancer (2016)
LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. In: Neural Computation (1989)
LeCun, Y., Bottou, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS 2012), vol. 25 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, US, 27–30 June 2016
Luttrell, S.: Hierarchical self-organising networks. In: 1989 First IEE International Conference on Artificial Neural Networks, (Conf. Publ. No. 313), London, UK (1989)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507, 28 Jul 2006
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA (2005)
Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision (1999)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv preprint, arXiv:1311.2524v5 [cs.CV], 22 Oct 2014
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. arXiv preprint arXiv:1506.02640v5 [cs.CV], 9 May 2016
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597v1 [cs.CV], 18 May 2015
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. arXiv preprint arXiv:1411.4038v2 [cs.CV], 8 Mar 2015
Pan, T., Wang, B., Ding, G., Yong, J.-H.: Fully convolutional neural networks with full-scale-features for semantic segmentation. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) (2017)
Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumour detection and segmentation using U-Net based fully convolutional networks. arXiv preprint arXiv:1705.03820v3 [cs.CV], 3 Jun 2017
Erden, B., Gamboa, N., Wood, S.: 3D Convolutional Neural Network for Brain Tumor Segmentation, Student Report in cs231n 526. Stanford University (2017)
MICCAI BRATS 2015 dataset. http://www.braintumorsegmentation.org/
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1928–1958 (2014)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic gradient descent. arXiv preprint arXiv:1412.6980v9 [cs.LG], 30 Jan 2017
Pinheiro, P.H.O., Collobert, R.: Recurrent convolutional neural networks for scene parsing. arXiv preprint arXiv:1306.2795v1 [cs.CV], 12 Jun 2013
Pinheiro, P.H.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. arXiv preprint arXiv:1411.6228v3 [cs.CV], 24 Apr 2015
Graves, A., Fernandez, S., Schmidhuber, J.: Multi-dimensional recurrent neural networks. arXiv preprint arXiv:0705.2011v1 [cs.AI], 14 May 2007
Stollenga, M.F., Byeon, W., Liwicki, M., Schmidhuber, J.: Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. arXiv preprint arXiv:1506.07452v1 [cs.CV], 24 Jun 2015
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
RMSProp. https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
Acknowledgements
We would like to show our gratitude towards MICCAI BraTS for providing their 2015 dataset as open source.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dutta, J., Chakraborty, D., Mondal, D. (2020). Multimodal Segmentation of Brain Tumours in Volumetric MRI Scans of the Brain Using Time-Distributed U-Net. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_62
Download citation
DOI: https://doi.org/10.1007/978-981-13-9042-5_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9041-8
Online ISBN: 978-981-13-9042-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)