[1]	https://www.britannica.com/science/brain 
[2]	B. H. Menze et al., "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)," IEEE Trans. Med. Imag., vol. 34, no. 10, pp. 1993-2024, Oct. 2015.
[3]	P. Gibbs, D. L. Buckley, S. J. Blackband, and A. Horsman, “Tumour volume determination from MR images by morphological segmentation,” Phys. Med. Biol., vol. 41, no. 11, pp. 2437-2446, 1996.
[4]	T. Imtiaz, S. Rifat, S. A. Fattah and K. A. Wahid, "Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images," IEEE Access, vol. 8, pp. 25335-25349, 2020.
[5]	N. Geschwind and W. Levitsky, “Human Brain: Left-right asymmetry in temporal speech region,” Science, vol. 161, pp. 186–187, 1968.
[6]	A. Kermi, K. Andjouh and F. Zidane, "Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets," IET Image Process., vol. 12, no. 11, pp. 1964-1971, 11 2018.
[7]	S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.
[8]	V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” Proc. of International Conference on Machine Learning, pp. 807–814, 2010.
[9]	A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” Proc. of International Conference on Machine Learning, vol. 30, p. 3, 2013.
[10]	M. N. Gibbs and D. J. MacKay, “Variational gaussian process classifiers,” IEEE Trans. Neural Networks, vol. 11, no. 6, pp. 1458–1464, 2000.
[11]	K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556, 2014.
[12]	V. Badrinarayanan, A. Kendall and R. Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 39, no. 12, pp. 2481-2495, 1 Dec. 2017.
[13]	C. Szegedy et al., "Going deeper with convolutions," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), June 2015, pp. 1-9.
[14]	C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), June 2016, pp. 2818-2826.
[15]	K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), June 2016, pp. 770-778.
[16]	G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), July 2017, pp. 2261-2269.
[17]	Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, p. 436-444, 2015.
[18]	D. Ciregan, U. Meier and J. Schmidhuber, "Multi-column deep neural networks for image classification," 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 3642-3649
[19]	Robbins, Herbert; Monro, Sutton. “A Stochastic Approximation Method,” Ann. Math. Statist., vol. 22, no. 3, pp. 400-407, 1951.
[20]	Diederik P. Kingma, Jimmy Ba, "Adam: A method for stochastic optimization", arXiv:1412.6980, 2014.
[21]	J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), June 2015, pp. 3431-3440.
[22]	O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Proc. MICCAI, Nov. 2015, pp. 234–241.
[23]	Haichun Li, Ao Li and Minghui Wang, “A novel end-to-end brain tumor segmentation method using improved fully convolutional networks,” Comput. Biol. Med., vol. 108, p. 150-160, May 2019.
[24]	Y. Ding, F. Chen, Y. Zhao, Z. Wu, C. Zhang and D. Wu, "A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmentation," IEEE Access, vol. 7, pp. 104011-104024, 2019.
[25]	L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara and D. Rueckert, "DRINet for Medical Image Segmentation," IEEE Trans. Med. Imag., vol. 37, no. 11, pp. 2453-2462, Nov. 2018.
[26]	S. Pereira, A. Pinto, J. Amorim, A. Ribeiro, V. Alves and C. A. Silva, "Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks," IEEE Trans. Med. Imag., May 2019. 
[27]	Y. Ding, C. Li, Q. Yang, Z. Qin and Z. Qin, "How to Improve the Deep Residual Network to Segment Multi-Modal Brain Tumor Images," IEEE Access, vol. 7, pp. 152821-152831, 2019.
[28]	K. Hu et al., "Brain Tumor Segmentation Using Multi-Cascaded Convolutional Neural Networks and Conditional Random Field," IEEE Access, vol. 7, pp. 92615-92629, Jul. 2019.
[29]	K. Kamnitsas, C. Ledig, V.F. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, B. Glocker, “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,” Med. Image Anal. vol. 36, pp. 61–78, 2017. 
[30]	Chen, Shengcong, Ding, Changxing and Liu, Minfeng, “Dual-force convolutional neural networks for accurate brain tumor segmentation,” Pattern Recognit., vol. 88, pp. 90-100, Apr. 2019. 
[31]	Zexun Zhou, Zhongshi He, Meifeng Shi, Jinglong Du and Dingding Chen, “3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads,” Comput. Biol. Med., vol. 121, June 2020.
[32]	Tuzikov, A., Colliot, O., Bloch, I. “Evaluation of the symmetry plane in 3D MR brain images,” Pattern Recognit. Lett., 2003, 24, pp. 2219–2233.
[33]	S. Prima, S. Ourselin and N. Ayache, "Computation of the mid-sagittal plane in 3-D brain images," IEEE Trans. Med. Imag., vol. 21, no. 2, pp. 122-138, Feb. 2002.
[34]	Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, April 2004.
[35]	L. Nyúl and J. Udupa, “On standardizing the MR image intensity scale,” Magn. Reson. Med., vol. 42, no. 6, pp. 1072–1081, 1999.
[36]	L. G. Nyúl, J. K. Udupa, and X. Zhang, “New variants of a method of MRI scale standardization,” IEEE Trans. Med. Imag., vol. 19, no. 2, pp. 143–150, Feb. 2000.
[37]	https://www.med.upenn.edu/sbia/brats2018/data.html 
[38]	S. Bakas et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117 
[39]	S. Bakas et al., "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 (2018) 
[40]	I. Loshchilov and F. Hutter, “Sgdr: stochastic gradient descent with restarts,” International Conference on Learning Representations 2016, 2016.
[41]	Yoav Benjamini and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” J. R. Stat. Soc. Series B Stat. Methodol., vol. 57, no. 1, pp. 289–300, 1995.
[42]	Ralph M. Richart M. D. “Evaluation of the true false negative rate in cytology,” Am. J. Obstet. Gynecol., vol. 89, no. 6, pp. 723-726, July 1964.