BrainLes 2015: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 195-208 | Cite as
A Convolutional Neural Network Approach to Brain Tumor Segmentation
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.
Keywords
Convolutional Neural Network Stochastic Gradient Descent Challenge Data Challenge Dataset Deep Convolutional Neural NetworkReferences
- 1.Brats challenge manuscripts (2014). http://www.braintumorsegmentation.org
- 2.Virtual skeleton database. http://www.virtualskeleton.ch/
- 3.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 Google Scholar
- 4.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)Google Scholar
- 5.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)Google Scholar
- 6.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. arXiv preprint (2013). arxiv:1308.4214
- 7.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)Google Scholar
- 8.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. arXiv preprint (2015). arxiv:1505.03540
- 9.Huang, G.B., Jain, V.: Deep and wide multiscale recursive networks for robust image labeling. ICLR (2014). arxiv:1310.0354
- 10.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)Google Scholar
- 11.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)CrossRefGoogle Scholar
- 12.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)Google Scholar
- 13.Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings of the BRATS-MICCAI (2014)Google Scholar