Abstract
Brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose a 3D deep neural network-based algorithm for joint brain tumor detection and intra-tumor structure segmentation, including necrosis, edema, non-enhancing and enhancing tumor, using multimodal magnetic resonance imaging sequences. An ensemble of cascaded U-Nets is designed to detect the tumor and a deep convolutional neural network is constructed for patch-based intra-tumor structure segmentation. This algorithm has been evaluated on the BraTS 2017 Challenge dataset and achieved Dice similarity coefficients of 0.81, 0.69 and 0.55 in the segmentation of whole tumor, core tumor and enhancing tumor, respectively. Our results suggest that the proposed algorithm has promising performance in automated brain tumor segmentation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mcguire, S.: World cancer report 2014. Geneva, Switzerland: world health organization, international agency for research on cancer, WHO Press, 2015. Adva. Nutr. 7, 418–419 (2016)
Agn, M., Puonti, O., Rosenschöld, P.M., Law, I., Van 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). https://doi.org/10.1007/978-3-319-30858-6_15
Cordier, N., Delingette, H., Ayache, N.: A patch-based approach for the segmentation of pathologies: application to glioma labelling. IEEE Trans. Med. Imaging 35, 1066–1076 (2016)
Cobzas, D., Birkbeck, N., Schmidt, M.W., Jagersand, M., Murtha, A.: 3D variational brain tumor segmentation using a high dimensional feature set. In: International Conference on Computer Vision, pp. 1–8 (2007)
Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31, 1941–1954 (2012)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_44
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). https://doi.org/10.1007/978-3-642-33454-2_46
Tustison, N.J., Shrinidhi, K.L., Wintermark, M., Durst, C.R., Kandel, B.M., Gee, J.C., Grossman, M.C., Avants, B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13, 209–225 (2015)
Havaei, M., Davy, A., Wardefarley, D., Biard, A., Courville, A.C., Bengio, Y., Pal, C., Jodoin, P., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251 (2016)
Zhao, L.Y., Jia, K.B.: Multiscale CNNs for brain tumor segmentation and diagnosis. Comput. Math. Methods Med. 2016, 1–7 (2016)
Konstantinos, K., Enzo, F., Sarah, P., Cristian, L., Aditya, N., Antonio, C., Daniel, R., Glocker, B.: DeepMedic on brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, vol. 10154, pp. 138–149. Springer, Cham (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 fields. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016, vol. 10154, pp. 75–87. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_8
Dong, H., Yang, G., Liu, F., Mo, Y., Guo Y.: Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: MIUA (2017)
Pereira, S., Oliveira, A., Alves, V., Silva, C.: On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: a preliminary study. In: 2017 IEEE 5th Portuguese Meeting on Bioengineering
BraTS 2017 Image Repository. http://www.med.upenn.edu/sbia/brats2017/data.html
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. Sci. Data 4 (2017). https://doi.org/10.1038/sdata.2017.117
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 preoperative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
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 preoperative scans of the TCGA-LGG collection. The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
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., 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., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
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)
Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61771397 and 61471297. We appreciate the efforts devoted by BraTS Challenge organizers to collect and share the data for comparing brain tumor segmentation algorithms for multimodality MR sequences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hu, Y., Xia, Y. (2018). 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-75238-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75237-2
Online ISBN: 978-3-319-75238-9
eBook Packages: Computer ScienceComputer Science (R0)