A REVIEW ON END-TO-END METHODS FOR BRAIN TUMOR SEGMENTATION AND OVERALL SURVIVAL PREDICTION

Details

Hits: 2413

Volume 3 (1), June 2020, Pages 119-138

Snehal R. Rajput, Mehul S. Raval

Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Brain tumor segmentation intends to delineate tumor tissues from healthy brain tissues. The tumor tissues include necrosis, peritumoral edema, and active tumor. In contrast, healthy brain tissues include white matter, gray matter, and cerebrospinal fluid. The MRI based brain tumor segmentation research is gaining popularity as; 1. It does not irradiate ionized radiation like X-ray or computed tomography imaging. 2. It produces detailed pictures of internal body structures. The MRI scans are input to deep learning-based approaches that are useful for automatic brain tumor segmentation. The features from segments are fed to the classifier, which predicts the overall survival of the patient. This paper aims to give an extensive overview of the state-of-the-art, jointly covering brain tumor segmentation and overall survival prediction.

Keywords:

Brain, image analysis, neural network, segmentation, tumor

DOI: https://doi.org/10.32010/26166127.2020.3.1.119.138

Reference 

Agravat, R. R., & Raval, M. S. (2019, October). Brain Tumor Segmentation and Survival Prediction. In International MICCAI Brainlesion Workshop (pp. 338-348). Springer, Cham.

Agravat, R. R., & Raval, M. S. (2019, October). Prediction of overall survival of brain tumor patients. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 31-35). IEEE.

Albiol, A., Albiol, A., & Albiol, F. (2018, September). Extending 2D deep learning architectures to 3D image segmentation problems. In International MICCAI Brainlesion Workshop (pp. 73-82). Springer, Cham.

Baid, U., Talbar, S., Rane, S., Gupta, S., et al. (2018, September). Deep learning radiomics algorithm for gliomas (drag) model: a novel approach using 3d unet based deep convolutional neural network for predicting survival in gliomas. In International MICCAI Brainlesion Workshop (pp. 369-379). Springer, Cham.

Bakas, S., Akbari, H., Sotiras, A., et al. (2017). Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data, 4, 170117.

Bakas, S., Reyes, M., Jakab, A., Bauer, S., et al. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.

Banerjee, S., Mitra, S., & Shankar, B. U. (2018, September). Multi-planar spatial-ConvNet for segmentation and survival prediction in brain cancer. In International MICCAI Brainlesion Workshop (pp. 94-104). Springer, Cham.

Feng, X., Tustison, N. J., Patel, S. H., et al. (2020). Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features. Frontiers in Computational Neuroscience, 14, 25.

Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059).

Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059).

Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426–1438.

Guo, X., Schwartz, L., & Zhao, B. (2013). Semi-automatic segmentation of multimodal brain tumor using active contours. Multimodal Brain Tumor Segmentation, 27-30.

Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017, September). Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In International MICCAI Brainlesion Workshop (pp. 287-297). Springer, Cham.

Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2018, September). No new-net. In International MICCAI Brainlesion Workshop (pp. 234-244). Springer, Cham.

Işın, A., Direkoğlu, C., & Şah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102, 317-324.

Jungo, A., McKinley, R., Meier, R., et al. (2017, September). Towards uncertainty-assisted brain tumor segmentation and survival prediction. In International MICCAI Brainlesion Workshop (pp. 474-485). Springer, Cham.

Jungo, A., McKinley, R., Meier, R., et al. (2017, September). Towards uncertainty-assisted brain tumor segmentation and survival prediction. In International MICCAI Brainlesion Workshop (pp. 474-485). Springer, Cham.

Kendall, A., Badrinarayanan, V., & Cipolla, R. (2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680.

Kendall, A., & Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems (pp. 5574-5584).

Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in neural information processing systems (pp. 6402-6413).

McKinley, R., Meier, R., & Wiest, R. (2018, September). Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In International MICCAI Brainlesion Workshop (pp. 456-465). Springer, Cham.

Menze, B. H., Jakab, A., Bauer, S., et al. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 34(10), 1993-2024.

Milletari, F., Navab, N., & Ahmadi, S. A. (2016, October). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) (pp. 565-571). IEEE.

Myronenko, A. (2018, September). 3D MRI brain tumor segmentation using autoencoder regularization. In International MICCAI Brainlesion Workshop (pp. 311-320). Springer, Cham.

Prastawa, M., Bullitt, E., Moon, N., et al. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors1. Academic radiology, 10(12), 1341-1348.

Preston, D. (2006, November 30). MRI Basics. Retrieved from: http://casemed.case.edu/clerkships/neurology/Web%20Neurorad/MRI%20Basics.htm

Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

Sauwen, N., Acou, M., Sima, D. M., et al. (2017). Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization. BMC Medical Imaging, 17(1,29).

Shboul, Z. A., Vidyaratne, L., Alam, M., & Iftekharuddin, K. M. (2017, September). Glioblastoma and survival prediction. In International MICCAI Brainlesion Workshop (pp. 358-368). Springer, Cham.

Sim, K. S., Nia, M. E., Tso, C. P., & Kho, D. T. K. (2016). Brain Ventricle Detection Using Hausdorff Distance. Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology, 523-531.

Sun, L., Zhang, S., & Luo, L. (2018, September). Tumor segmentation and survival prediction in glioma with deep learning. In International MICCAI Brainlesion Workshop (pp. 83-93). Springer, Cham.

Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022.

Vaidhya, K., Thirunavukkarasu, S., Alex, V., & Krishnamurthi, G. (2015, October). Multi-modal brain tumor segmentation using stacked denoising autoencoders. In BrainLes 2015 (pp. 181-194). Springer, Cham.

Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2018, September). Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation. In International MICCAI Brainlesion Workshop (pp. 61-72). Springer, Cham.

Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2018, September). Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation. In International MICCAI Brainlesion Workshop (pp. 61-72). Springer, Cham.

Wang, G., Li, W., Vercauteren, T., & Ourselin, S. (2019). Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Frontiers in computational neuroscience, 13, 56.

Weninger, L., Rippel, O., Koppers, S., & Merhof, D. (2018, September). Segmentation of brain tumors and patient survival prediction: methods for the BraTS 2018 challenge. In International MICCAI Brainlesion Workshop (pp. 3-12). Springer, Cham.

Wu, Y., & He, K. (2018). Group normalization. In Proceedings of the European conference on computer vision (ECCV) (pp. 3-19).