Multi-scale Networks for Segmentation of Brain Magnetic Resonance Images
Measuring the distribution of major brain tissues using magnetic resonance (MR) images has attracted extensive research efforts. Due to its remarkable success, deep learning-based image segmentation has been applied to this problem, in which the size of patches usually represents a tradeoff between complexity and accuracy. In this paper, we propose the multi-size-and-position neural network (MSPNN) for brain MR image segmentation. Our contributions include (1) jointly using U-Nets trained on large patches and back propagation neural networks (BPNNs) trained on small patches for segmentation, and (2) adopting the convolutional auto-encoder (CAE) to restore MR images before applying them to BPNNs. We have evaluated this algorithm against five widely used brain MR image segmentation approaches on both synthetic and real MR studies. Our results indicate that the proposed algorithm can segment brain MR images effectively and provide precise distribution of major brain tissues.
KeywordsBrain MR image segmentation Deep learning U-Net Convolutional Auto-Encoder (CAE) Back Propagation Neural Network (BPNN)
This work was supported by the National Natural Science Foundation of China under Grants 61471297.
- 5.Ouarda, A., Fadila, B.: Improvement of MR brain images segmentation based on interval type-2 fuzzy C-Means. In: Third World Conference on Complex Systems (2016)Google Scholar
- 7.Su, C.M., Chang, H.H.: A level set based deformable model for segmentation of human brain MR images. In: IEEE International Conference on Biomedical Engineering and Informatics, pp. 105–109 (2014)Google Scholar
- 9.Brébisson, A.D., Montana, G.: Deep neural networks for anatomical brain segmentation. In: Computer Vision and Pattern Recognition Workshops, pp. 20–28 (2015)Google Scholar
- 11.Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1337–1342 (2015)Google Scholar
- 12.Nie, D., Wang, L., Gao, Y., Sken, D.: Fully convolutional networks for multi-modality isointense infant brain image segmentation. In: IEEE International Symposium on Biomedical Imaging, pp. 1342–1345 (2016)Google Scholar
- 13.Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage (2017) Google Scholar
- 14.Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)Google Scholar
- 15.Rumelhart, D., Mcclelland, J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. MIT Press, Cambridge (1986)Google Scholar
- 16.Masci, J., Meier, U., Dan, C., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: International Conference on Artificial Neural Networks, pp. 52–59 (2011)Google Scholar
- 18.Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E.J., Johansen-Berg, H., Bannister, P.R., Luca, M.D., Drobnjak, I., Flitney, D.E.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl. 1), S208–S219 (2004)CrossRefGoogle Scholar
- 20.School, M.G.H.H.M.: The Internet Brain Segmentation Repository (IBSR). http://www.cma.mgh.harvard.edu/ibsr/index.html
- 22.Bharatha, A., Hirose, M., Hata, N., Warfield, S.K., Ferrant, M., Zou, K.H., Suarez-Santana, E., Ruiz-Alzola, J., Amico, A.D., Cormack, R.A.: Evaluation of three-dimensional finite element-based deformable registration of pre- and intra-operative prostate imaging. Med. Phys. 28(12), 2551–2560 (2001)CrossRefGoogle Scholar