Abstract
The aim of this paper is to integrate some advanced statistical methods with modern deep learning methods for tumor segmentation and survival time prediction in the BraTS 2017 challenge. The goals of the BraTS 2017 challenge are to utilize multi-institutional pre-operative MRI scans to segment out different tumor subregions and then to use tumor information to predict patient’s overall survival. We build a two-phase patch-based convolutional neural network (TPCNN) model to classify all the pixels in the brain and further refine the segmentation results by using XGBoost and a post-processing procedure. The segmentation results are then used to extract various informative radiomic features for prediction of the survival time by using the XGBoost method.
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References
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
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)
Kamnitsas, K., Ledig, C., Newcombe, V.F.J., 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)
Liu, Y., Xu, X., Yin, L., Zhang, X., Li, L., Lu, H.: Relationship between Glioblastoma Heterogeneity and survival time: an MR imaging texture analysis. Am. J. Neuroradiol. 38, 1695–1701 (2017)
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)
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)
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
Bakas, S., Sotiras, A., Akbari, H., Rozycki, M., Bilello, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017)
Bakas, S., Akbari, H., Bilello, M., Sotiras, A., Rozycki, M., Freymann, J.B., Kirby, J.S., Farahani, K., Davatzikos, C.: Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nature Scientific Data (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Sutton, C., McCallum, A., et al.: An introduction to conditional random fields. Found. Trends®in Mach. Learn. 4(4), 267–373 (2012)
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(2), 209–225 (2015)
Wangaryattawanich, P., Wang, J., Thomas, G.A., Chaddad, A., Zinn, P.O., Colen, R.R.: Survival analysis of pre-operative GBM patients by using quantitative image features. In: 2014 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 625–627. IEEE (2014)
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. arXiv preprint arXiv:1702.04528 (2017)
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Zhou, F., Li, T., Li, H., Zhu, H. (2018). TPCNN: Two-Phase Patch-Based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction. 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_24
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DOI: https://doi.org/10.1007/978-3-319-75238-9_24
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