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2018 | OriginalPaper | Buchkapitel

Dilated Convolutions for Brain Tumor Segmentation in MRI Scans

verfasst von : Marc Moreno Lopez, Jonathan Ventura

Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Verlag: Springer International Publishing

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Abstract

We present a novel method to detect and segment brain tumors in Magnetic Resonance Imaging scans using a novel network based on the Dilated Residual Network. Dilated convolutions provide efficient multi-scale analysis for dense prediction tasks without losing resolution by downsampling the input. To the best of our knowledge, our work is the first to evaluate a dilated residual network for brain tumor segmentation in magnetic resonance imaging scans. We train and evaluate our method on the Brain Tumor Segmentation (BraTS) 2017 challenge dataset. To address the severe label imbalance in the data, we adopt a balanced, patch-based sampling approach for training. An ablation study establishes the importance of residual connections in the performance of our network.

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Vorheriges Kapitel A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

Nächstes Kapitel Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation

This is actually a cross-correlation but we call it a convolution as is common in the literature today.

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. In: arXiv preprint arXiv:1606.00915 (2016)

Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE (2012)

Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 2843–2851 (2012)

Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240. ACM, Pittsburgh, Pennsylvania, USA (2006). http://doi.acm.org/10.1145/1143844.1143874, https://doi.org/10.1145/1143844.1143874, ISBN 1-59593-383-2

Duchi, J.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH

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. In: CoRR abs/1505.03540 (2015). http://arxiv.org/abs/1505.03540

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). arXiv: 1505.03540 CrossRef

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)

10.

Isensee, F., Kickingereder, P., Bonekamp, D., Bendszus, M., Wick, W., Schlemmer, H.-P., Maier-Hein, K.: Brain tumor segmentation using large receptive field deep convolutional neural networks. Bildverarbeitung für die Medizin 2017. Informatik aktuell, pp. 86–91. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54345-0_24 CrossRef

11.

Işin, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. In: 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, Procedia Computer Science, 29–30 August 2016, Vienna, Austria, vol. 102, no. Supplement C, pp. 317–324 (2016). http://www.sciencedirect.com/science/article/pii/S187705091632587X, https://doi.org/10.1016/j.procs.2016.09.407, ISSN 1877–0509

12.

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 Segmentation 13, 46 (2015)

13.

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)CrossRef

14.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2015). arXiv: 1605.06211

15.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

16.

Bjoern, M., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B., Ayache, N., Buendia, P., Collins, L., Cordier, N., Corso, J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Christopher, D., Michel, D., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K., Jena, R., John, N., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Riklin-Raviv, T., Reza, S., Ryan, M., Schwartz, L., Shin, H.-C., Shotton, J., Silva, C., Sousa, N., Subbanna, N., Szekely, G., Taylor, T., Thomas, O., Tustison, N., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K.: The multi-modal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef

17.

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)CrossRef

18.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28 CrossRef

19.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017)CrossRef

20.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. In: The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q

21.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. In: The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

22.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: arXiv preprint arXiv: 1409.1556 (2014)

23.

Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (ICLR) (2016). arXiv: 1511.07122v3

24.

Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Computer Vision and Pattern Recognition (CVPR) (2017). arXiv: 1705.09914

Titel: Dilated Convolutions for Brain Tumor Segmentation in MRI Scans
verfasst von: Marc Moreno Lopez
Jonathan Ventura
Verlag: Springer International Publishing
Buch: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-319-75237-2

Electronic ISBN: 978-3-319-75238-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-75238-9_22

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