Abstract
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images.
In this study we use an adversarial training approach to improve CNN-based brain MRI segmentation. To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations. During training, this loss function is optimised together with the conventional average per-voxel cross entropy loss.
The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dai, W., Doyle, J., Liang, X., Zhang, H., Dong, N., Li, Y., **ng, E.P.: SCAN: structure correcting adversarial network for chest X-rays organ segmentation. ar**v preprint ar**v:1703.08770 (2017)
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016)
Ghafoorian, M., Karssemeijer, N., Heskes, T., Bergkamp, M., Wissink, J., Obels, J., Keizer, K., de Leeuw, F.E., van Ginneken, B., Marchiori, E., Platel, B.: Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin. NeuroImage. Clin. 14, 391–399 (2017)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (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. Med. Image Anal. 35, 18–31 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T., Shen, D. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 597–609. Springer, Cham (2017). doi:10.1007/978-3-319-59050-9_47
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Kohl, S., Bonekamp, D., Schlemmer, H.P., Yaqubi, K., Hohenfellner, M., Hadaschik, B., Radtke, J.P., Maier-Hein, K.: Adversarial networks for the detection of aggressive prostate cancer. ar**v preprint ar**v:1702.08014 (2017)
Landman, B.A., Ribbens, A., Lucas, B., Davatzikos, C., Avants, B., Ledig, C., Ma, D., Rueckert, D., Vandermeulen, D., Maes, F., Erus, G., Wang, J., Holmes, H., Wang, H., Doshi, J., Kornegay, J., Manjon, J., Hammers, A., Akhondi-Asl, A., Asman, A.J., Warfield, S.K.: MICCAI 2012 Workshop on Multi-Atlas Labeling. CreateSpace Independent Publishing Platform, Nice (2012)
Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. In: NIPS Workshop on Adversarial Training (2016)
Mendrik, A.M., Vincken, K.L., Kuijf, H.J., Breeuwer, M., Bouvy, W.H., de Bresser, J., Alansary, A., de Bruijne, M., Carass, A., El-Baz, A., Jog, A., Katyal, R., Khan, A.R., van der Lijn, F., Mahmood, Q., Mukherjee, R., van Opbroek, A., Paneri, S., Pereira, S., et al.: MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans. Comput. Intel. Neurosci. 2015 (2015). Article No. 813696
Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J., Išgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imag. 35(5), 1252–1261 (2016)
Moeskops, P., Wolterink, J.M., Velden, B.H.M., Gilhuijs, K.G.A., Leiner, T., Viergever, M.A., Išgum, I.: Deep learning for multi-task medical image segmentation in multiple modalities. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 478–486. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_55
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)
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(1), 1929–1958 (2014)
Wolterink, J.M., Leiner, T., Viergever, M.A., IÅ¡gum, I.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imag. (2017). https://doi.org/10.1109/TMI.2017.2708987
Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR -2016. LNCS, vol. 10129, pp. 95–102. Springer, Cham (2017). doi:10.1007/978-3-319-52280-7_9
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., Shen, D.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)
Acknowledgements
The authors would like to thank the organisers of MRBrainS13 and the multi-atlas labelling challenge for providing the data. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Moeskops, P., Veta, M., Lafarge, M.W., Eppenhof, K.A.J., Pluim, J.P.W. (2017). Adversarial Training and Dilated Convolutions for Brain MRI Segmentation. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-67558-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67557-2
Online ISBN: 978-3-319-67558-9
eBook Packages: Computer ScienceComputer Science (R0)