Abstract
We consider the problem of fully automatic brain focal pathology segmentation, in MR images containing low and high grade gliomas and ischemic stroke lesion. We propose a Convolutional Neural Network (CNN) approach which is amongst the top performing methods while also being extremely computationally efficient, a balance that existing methods have struggled to achieve. Our CNN is trained directly on the image modalities and thus learns a feature representation directly from the data. We propose a cascaded architecture with two pathways: one which focuses on small details in gliomas and one on the larger context. We also propose a two-phase patch-wise training procedure allowing us to train models in a few hours. Fully exploiting the convolutional nature of our model also allows us to segment a complete brain image in 25 s to 3 min. Experimental results on BRain Tumor Segmentation challenges (BRATS’13, BRATS’15) and Ischemic Stroke Lesion Segmentation challenge (ISLES’15) reveal that our approach is among the most accurate in the literature, while also being computationally very efficient.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Brats challenge manuscripts (2014). http://www.braintumorsegmentation.org
Virtual skeleton database. http://www.virtualskeleton.ch/
Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ants). Insight J. 2, 1–35 (2009). http://papers.nips.cc/paper/4741-deep-neural-networks-segment-neuronal-membranes-in-electron-microscopy-images.pdf
Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2843–2851. Curran Associates, Inc (2012)
Davy, A., Havaei, M., Warde-Farley, D., Biard, A., Tran, L., Jon, P.M., Courville, A., Larochelle, H., Pal, C., Bengio, Y.: Brain tumor segmentation with deep neural networks. In: Proceedings of the BRATS-MICCAI (2014)
Goodfellow, I.J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., Bengio, Y.: Pylearn2: a machine learning research library. ar**v preprint (2013). arxiv:1308.4214
Havaei, M., Jon, P.M., Larochelle, H.: Efficient interactive brain tumor segmentation as within-brain knn classification. In: International Conference on Pattern Recognition (ICPR) (2014)
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. ar**v preprint (2015). arxiv:1505.03540
Huang, G.B., Jain, V.: Deep and wide multiscale recursive networks for robust image labeling. ICLR (2014). arxiv:1310.0354
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Menze, B., 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 34(10), 1993–2024 (2015)
Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J.: Multi-modal brain tumor segmentation using deep convolutional neural networks. In: Proceedings of the BRATS-MICCAI (2014)
Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings of the BRATS-MICCAI (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, PM. (2016). A Convolutional Neural Network Approach to Brain Tumor Segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-30858-6_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30857-9
Online ISBN: 978-3-319-30858-6
eBook Packages: Computer ScienceComputer Science (R0)