Abstract
Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.
K. Kamnitsas—Part of this work was carried on when KK was an intern at Microsoft.
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Notes
- 1.
Code publicly available at: https://github.com/Kamnitsask/deepmedic.
- 2.
Although these experiments were performed with the original version of the network, we expect the trends to continue after the extension with residual connections.
- 3.
Code of the 3D CRF available at: https://github.com/Kamnitsask/dense3dCrf/.
References
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 (2016)
Mazzara, G.P., Velthuizen, R.P., Pearlman, J.L., Greenberg, H.M., Wagner, H.: Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int. J. Radiat. Oncol. Biol. Phys. 59(1), 300–312 (2004)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)
Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 532–540. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23629-7_65
Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 651–658. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33418-4_80
Zikic, D., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_46
Tustison, N., Wintermark, M., Durst, C., Brian, A.: ANTs and arboles. In: Proceedings of BRATS-MICCAI (2013)
Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J.: Multi-modal brain tumor segmentation using deep convolutional neural networks. In: Proceedings of BRATS-MICCAI (2014)
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Bakas, S., et al.: GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 144–155. Springer, Cham (2016). doi:10.1007/978-3-319-30858-6_13
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 arXiv:1505.03540 (2015)
Lyksborg, M., Puonti, O., Agn, M., Larsen, R.: An ensemble of 2D convolutional neural networks for tumor segmentation. In: Paulsen, R.R., Pedersen, K.S. (eds.) SCIA 2015. LNCS, vol. 9127, pp. 201–211. Springer, Cham (2015). doi:10.1007/978-3-319-19665-7_17
Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., Glocker, B.: Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. Ischemic Stroke Lesion Segm., 13–16 (2015)
Maier, O., Menze, B.H., von der Gablentz, J., Häni, L., Heinrich, M.P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., et al.: ISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)
Alansary, A., et al.: Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 589–597. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_68
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. arXiv preprint arXiv:1603.05027 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. Adv. Neural Inf. Process. Syst. 24, 109–117 (2011)
Chang, P.D.: Fully convolutional neural networks with hyperlocal features for brain tumor segmentation. In: Proceedings of BRATS-MICCAI (2016)
Le Folgoc, L., Nori, A.V., Alvarez-Valle, J., Lowe, R., Criminisi, A.: Segmentation of brain tumors via cascades of lifted decision forests. In: Proceedings of BRATS-MICCAI (2016)
Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., Zhang, Y.: Brain tumor segmentation using a fully convolutional neural network with conditional random field. In: Proceedings of BRATS-MICCAI (2016)
Zeng, K., Bakas, S., Sotiras, A., Akbari, H., Rozycki, M., Rathore, S., Pati, S., Davatzikos, C.: Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework. In: Proceedings of BRATS-MICCAI (2016)
Meier, R., Knecht, U., Wiest, R., Reyes, M.: CRF-based brain tumor segmentation: alleviating the shrinking bias. In: Proceedings of BRATS-MICCAI (2016)
Acknowledgements
This work is supported by the EPSRC (grant No: EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http://www.tbicare.eu/; CENTER-TBI: https://www.center-tbi.eu/). Part of this work was carried on when KK was an intern at Microsoft Research Cambridge. KK is also supported by the President’s PhD Scholarship of Imperial College London. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.
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Kamnitsas, K. et al. (2016). DeepMedic for Brain Tumor Segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_14
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