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2016 | OriginalPaper | Buchkapitel

Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation

verfasst von : Peter D. Chang

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

Verlag: Springer International Publishing

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Abstract

In this paper, a fully convolutional residual neural network (FCR-NN) based on linear identity mappings is implemented for medical image segmentation, employed here in the setting of brain tumors. Inspired by deep residual networks which won the ImageNet ILSVRC 2015 classification challenge, the FCR-NN combines optimization gains from residual identity mappings with a fully convolutional architecture for image segmentation that efficiently accounts for both low- and high-level image features. After training two separate networks, one for the task of whole tumor segmentation and a second for tissue sub-region segmentation, the serial FCR-NN architecture exceeds state-of-the art with complete tumor, core tumor and enhancing tumor validation Dice scores of 0.87, 0.81 and 0.72 respectively. Despite each FCR-NN comprising a complex 22 layer architecture, the fully convolutional design allows for complete segmentation of a tumor volume within 2 s.

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Vorheriges Kapitel CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

Nächstes Kapitel Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks (2012)

Simonyan, K., Vedaldi, A., Zisserman, A.: Networks, deep inside convolutional: visualising image classification models and saliency maps. In: ICLR, p. 1 (2014)

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12 June, 1–9 Sep 2015 (2015)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 7(3), 171–180 (2015). Arxiv.Org

Ciresan, D., Giusti, A.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 1–9 (2012)

Yang, S., Ramanan, D.: Multi-scale recognition with DAG-CNNs (2015)

Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014)

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2014)

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). doi:10.1007/978-3-319-24574-4_28 CrossRef

10.

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: Proceedings of MICCAI-BRATS (Multimodal Brain Tumor Segmentation Challenge), pp. 29–33 (2015)

11.

Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-hein, K., Bendszus, M., Biller, A.: NeuroImage deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 129, 460–469 (2016)CrossRef

12.

Chen, H., Dou, Q., Yu, L., Heng, P.-A.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation, pp. 1–9 (2016). arXiv:1608.05895v1

13.

Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A novel content-based active contour model for brain tumor segmentation. Magn. Reson. Imaging 30(5), 694–715 (2012)CrossRef

14.

Harati, V., Khayati, R., Farzan, A.: Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images. Comput. Biol. Med. 41(7), 483–492 (2011)CrossRef

15.

Prastawa, M., Bullitt, E., Gerig, G.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13(2), 297–311 (2009)CrossRef

16.

Menze, B.H., Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 151–159. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15745-5_19 CrossRef

17.

Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G.: Tumor - cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging 31(3), 790–804 (2012)

18.

Zhu, Y., Young, G.S., Xue, Z., Huang, R.Y., You, H., Setayesh, K., Hatabu, H., Cao, F., Wong, S.T.: Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation. Acad. Radiol. 19(8), 977–85 (2012)

19.

Menze, B.H., Geremia, E., Ayache, N., Szekely, G.: Segmenting glioma in multi-modal images using a generative-discriminative model for brain lesion segmentation. In: Proceedings of MICCAI-BRATS (Multimodal Brain Tumor Segmentation Challenge), p. 7 (2012)

20.

Meier, R., Reyes, M., Bauer, S., Slotboom, J., Wiest, R.: A hybrid model for multimodal brain tumor segmentation. In: Proceedings of NCI-MICCAI BRATS (Multimodal Brain Tumor Segmentation Challenge), pp. 31–37 (2013)

21.

Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., Davatzikos, C.: Segmentation of gliomas in multimodal magnetic resonance imaging volumes based on a hybrid generative - discriminative framework. In: Proceedings of MICCAI-BRATS (Multimodal Brain Tumor Segmentation Challenge), pp. 5–12 (2015)

22.

Menze, B.H., Van Leemput, K., Lashkari, D., Riklin-Raviv, T., Geremia, E., Alberts, E., Gruber, P., Wegener, S., Weber, M.-A., Szekely, G., Ayache, N., Golland, P.: A generative probabilistic model and discriminative extensions for brain lesion segmentation with application to tumor and stroke. IEEE Trans. Med. Imaging 35(4), 933–946 (2016)CrossRef

23.

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 CrossRef

24.

Zeng, J., See, A.P., Phallen, J., Jackson, C.M., Belcaid, Z., Ruzevick, J., Durham, N., Meyer, C., Harris, T.J., Albesiano, E., Pradilla, G., Ford, E., Wong, J., Hammers, H.-J., Mathios, D., Tyler, B., Brem, H., Tran, P.T., Pardoll, D., Drake, C.G., Lim, M.: Anti-PD-1 blockade and stereotactic radiation produce long-term survival in mice with intracranial gliomas. Int. J. Rad. Oncol. Biol. Phys. 86(2), 343–349 (2013)

25.

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

26.

Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 131–143. Springer, Cham (2016). doi:10.1007/978-3-319-30858-6_12 CrossRef

27.

Lecun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series (1995)

28.

Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines (2010)

29.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, pp. 1–11 (2015). arXiv:1502.03167

30.

Bengio, Y., Boulanger-Lewandowski, N., Pascanu, R.: Advances in optimizing recurrent networks. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 8624–8628 (2013)

31.

He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification (2015)

32.

Mandic, D.P.: A generalized normalized gradient descent algorithm. IEEE Signal Process. Lett. 11(2), 115–118 (2004)CrossRef

33.

Vedaldi, A., Lenc, K.: MatConvNet. In: Proceedings of the 23rd ACM International Conference on Multimedia - MM 2015, pp. 689–692 (2015)

34.

Agn, M., Puonti, O., Rosenschöld, P.M., Law, I., Leemput, K.: Brain tumor segmentation using a generative model with an RBM prior on tumor shape. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 168–180. Springer, Cham (2016). doi:10.1007/978-3-319-30858-6_15 CrossRef

35.

Menze, B.H., 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.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, Ç., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R., Reza, S.M.S., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Leemput, K.V.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

Titel: Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation
verfasst von: Peter D. Chang
Verlag: Springer International Publishing
Buch: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-319-55523-2

Electronic ISBN: 978-3-319-55524-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-55524-9_11

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