Data Augmentation for Improved Brain Tumor Segmentation

References

• E. S. A. El-Dahshan, H. M. Mohsen, K. Revett, and A. M. Salem, “Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm,” Expert. Syst. Appl., Vol. 41, no. 11, pp. 5526–45, 2014.
   Web of Science ®Google Scholar
• M. Rezaei, K. Harmuth, W. Gierke, T. Kellermeier, M. Fischer, H. Yang, and C. Meinel, “A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor,” in Proceedings of International MICCAI Brainlesion Workshop, Springer, pp. 241–252, 2017.
   Google Scholar
• H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging, Vol. 35, no. 5, pp. 1170–81, 2016.
   PubMed Web of Science ®Google Scholar
• I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. W.- Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Proceedings of 27th International Conference on Neural Information Processing Systems, Vol. 2, pp. 2672–80, 2014.
   Google Scholar
• S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 39, no. 6, pp. 1137–49, 2017.
   PubMed Web of Science ®Google Scholar
• W. Zhu, X. Xiang, T. D. Tran, G. D. Hager, and X. Xie, “Adversarial deep structured nets for mass segmentation from mammograms,” in Proceedings of IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, pp. 847–50.
   Google Scholar
• P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5967–76.
   Google Scholar
• A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier GANs,” in Proceedings of 34th International Conference on Machine Learning, PMLR 70, 2017, pp. 2642–51.
   Google Scholar
• I. D. Bolano, and G. S. Torres, “A clustering-based method for brain tumor segmentation,” Contemporary Engineering Sciences, Vol. 9, no. 15, pp. 743–54, 2016.
   Google Scholar
• M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochella, “Brain tumor segmentation with deep neural networks,” Med. Image Anal., Vol. 35, pp. 18–31, 2017.
   PubMed Web of Science ®Google Scholar
• A. Chaddad, and C. Tanougast, “Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images,” Brain. Inform., Vol. 3, pp. 53–61, 2016.
   PubMedGoogle Scholar
• A. Chaddad, “Automated feature extraction in brain tumor by magnetic resonance imaging using Gaussian mixture models,” Int. Journal of Biomed. Imaging, Vol. 2015. Article ID 868031. DOI: 10.1155/2015/868031.
   PubMed Web of Science ®Google Scholar
• C. Sompong, and S. Wongthanavasu, “An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm,” Expert. Syst. Appl., Vol. 72, pp. 231–44, 2017.
   Web of Science ®Google Scholar
• P. T. Krishnan, P. Balasubramanian, and C. Krishnan, “Segmentation of Brain regions by integrating meta heuristic multilevel threshold with markov random field,” Curr. Med. Imaging. Rev., Vol. 12, no. 1, pp. 4–12, 2016.
   Web of Science ®Google Scholar
• F. Tavakoli, and J. Ghasemi, “Brain MRI segmentation by combining different MRI modalities using dempster–shafer theory,” IET Image Proc., Vol. 12, no. 8, pp. 1322–30, 2018.
   Web of Science ®Google Scholar
• B. G. Despotovic, B. Goossens, and W. Philips, “MRI segmentation of the human brain: challenges, methods, and applications,” Comput. Math. Methods Med., Vol. 2015. Article ID 450341. DOI: 10.1155/2015/450341.
   PubMed Web of Science ®Google Scholar
• P. Costa, A. Galdran, M. I. Meye, M. Niemeijer, M. Abràmoff, A. M. Mendonça, and A. Campilho, “End-to-End adversarial retinal image synthesis,” IEEE Trans. Med. Imaging, Vol. 37, no. 3, pp. 781–91, 2018.
   PubMed Web of Science ®Google Scholar
• W. Dai, N. Dong, Z. Wang, X. Liang, H. Zhang, and E. P. Xing, “SCAN: structure correcting adversarial network for organ segmentation in chest X-rays,” in Proceedings of Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, DLMIA 2018, ML-CDS 2018, pp. 263–7.
   Google Scholar
• B. H. Menze, et al., “The multimodal brain tumor image segmentation benchmark (BRATS),” IEEE Trans. Med. Imaging, Vol. 34, no. 10, pp. 1993–2024, 2015.
   PubMed Web of Science ®Google Scholar
• D. P. Kingma, and M. Welling, “Auto-encoding variational Bayes,” in Proceedings of International Conference on Learning Representations (ICLR 2014), Banff, AB, Canada, April 14–16, 2014.
   Google Scholar
• L. Mescheder, S. Nowozin, and A. Geiger, “Adversarial variational Bayes: unifying variational autoencoders and generative adversarial networks,” in Proeedings of 34th International Conference on Machine Learning, Vol. 70, Aug. 2017, pp. 2391–400.
   Google Scholar
• D. Mahapatra, B. Bozorgtabar, S. Hewavitharanage, and R. Garnavi, “Image super resolution using generative adversarial networks and local saliency maps for retinal image analysis,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2017, Sep. 2017, pp. 382–90.
   Google Scholar
• T. Schlegl, P. Seebock, S. M. Waldstein, U. S. Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in Proceedings of International Conference on Information Processing in Medical Imaging 2017, May 2017, pp. 146–57.
   Google Scholar
• D. Nie, R. Trullo, J. Lian, C. Petitjean, S. Ruan, Q. Wang, and D. Shen, “Medical image synthesis with context-aware generative adversarial networks,” in Proceedings of International Conference Medical Image Computing and Computer-Assisted Intervention –MICCAI 2017, Sep. 2017, pp. 417–25.
   Google Scholar
• Q. Li, Z. Yu, Y. Wang, and H. Zheng, “TumorGAN: a multi-modal data augmentation framework for brain tumor segmentation,” Sensors, Vol. 20, no. 15, pp. 4203, 2020. DOI: 10.3390/s20154203.
   PubMed Web of Science ®Google Scholar
• Jayadeva, S. Soman, and S. Saxena, “Eigensample: a non-iterative technique for adding samples to small datasets,” Appl. Soft. Comput., Vol. 70, pp. 1064–77, 2018.
   Web of Science ®Google Scholar
• K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in Chapter VIII.5, Graphics gems IV, P.S. Heckbert, Ed., Cambridge, MA: Academic Press, 1994, pp. 474–85.
   Google Scholar
• M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” in Proceedings of 34th International on Conference on Machine learning, PMLR 70, 2017, pp. 214–23.
   Google Scholar
• D. Shen, G. Wu, and H. I. Suk, “Deep learning in medical image analysis,” Annu. Rev. Biomed. Eng., Vol. 19, pp. 221–48, Jun 2017.
   PubMed Web of Science ®Google Scholar
• C. Villani, “The Wasserstein distances,” in Optimal Transport. Grundlehren der mathematischen Wissenschaften (A Series of Comprehensive Studies in Mathematics), Vol 338, Berlin, Heidelberg: Springer, 2009, pp. 93–111. DOI:10.1007/978-3-540-71050-9_6.
   Google Scholar
• O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-NET: learning dense volumetric segmentation from sparse annotation,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention –MICCAI 2016, pp. 424–32.
   Google Scholar
• T. Salimans, and D. P. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” Proc. Advances in Neural Information Processing Systems, Vol. 30, pp. 901–9, Dec. 2016.
   Google Scholar
• T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Adv. Neural. Inf. Process. Syst., Vol. 30, pp. 2234–42, Dec. 2016.
   Google Scholar
• A. V. Nasrulloh, C. G. Willcocks, P. T. G. Jackson, C. Geenen, M. S. Habib, and D. H. W. Steel, “Multi-Scale segmentation and surface fitting for measuring 3-D macular holes,” IEEE Trans. Med. Imaging, Vol. 37, no. 2, pp. 580–9, 2018.
   PubMed Web of Science ®Google Scholar
• A. Bousselham, O. Bouattane, M. Youssfi, and A. Raihani, “Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area,” Int. J. Biomed. Imaging., Vol. 2019, pp. Article ID 1758948, 2019. DOI: 10.1155/2019/1758948.
   PubMed Web of Science ®Google Scholar
• H. C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole, and M. Michalski, “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” in Proceedings of International Workshop on Simulation and Synthesis in Medical Imaging, pp. 1–11, 2018.
   Google Scholar
• G. Wang, W. Li, S. Ourselin, and T. Vercauteren, “Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks,” in Proceedings of Brainlesion: glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2017), pp. 178–90.
   Google Scholar
• A. R. Choudhury, R. Vanguri, S. R. Jambawalikar, and P. Kumar, “Segmentation of Brain tumors using DeepLabv3+,” in Proceedings of Brainlesion: glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2018), pp. 154–67.
   Google Scholar
• G. Sandhya, G. B. Kande T, and S. Savithri, “Tumor segmentation by a self-organizing-Map based Active Contour Model (SOMACM) from the brain MRIs,” IETE. J. Res. Jun. 2020. DOI: 10.1080/03772063.2020.1782780.
   Web of Science ®Google Scholar