275
Views
7
CrossRef citations to date
0
Altmetric
Research Article

Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)

, ORCID Icon, , &

References

  • Abdel-Maksoud, E., M. Elmogy, and R. Al-Awadi. 2015. Brain tumor segmentation based on a hybrid clustering technique." Egyptian Informatics Journal 16 (1):71–81. doi: 10.1016/j.eij.2015.01.003.
  • Amin, J., M. Sharif, M. Yasmin, T. Saba, M. A. Anjum, and S. L. Fernandes. 2019. A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning. Journal of Medical Systems 43 (11):326 doi: 10.1007/s10916-019-1453-8.
  • Bakas, S., M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, R. T. Shinohara, C. Berger, S. M. Ha, and M. Rozycki. 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.
  • Bauer, S., R. Wiest, L.-P. Nolte, and M. Reyes. 2013. A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58 (13):R97–129. doi: 10.1088/0031-9155/58/13/R97.
  • Çiçek, Ö., A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation. Paper presented at the International Conference on Medical Image Computing and Computer-assisted Intervention, 424–32, Springer, Cham.
  • Cirillo, M. D., D. Abramian, and A. Eklund. 2020. Vox2Vox: 3D-GAN for Brain Tumour Segmentation. arXiv Preprint arXiv 2003.13653.
  • Fernandes, S. L., U. J. Tanik, V. Rajinikanth, and K. A. Karthik. 2020. A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Computing and Applications 32 (20):15897–908. doi: 10.1007/s00521-019-04369-5.
  • Hai, J., K. Qiao, J. Chen, H. Tan, J. Xu, L. Zeng, D. Shi, and B. Yan. 2019. Fully convolutional densenet with multiscale context for automated breast tumor segmentation. Journal of Healthcare Engineering 2019:1–11. doi: 10.1155/2019/8415485.
  • Hameurlaine, M., and A. Moussaoui. 2019. Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging. Nano Biomed. Eng 11 (2):178–91.
  • Huang, P. D., Li, Z. Jiao, D. Wei, G. Li, Q. Wang, H. Zhang, and D. Shen. 2019. CoCa-GAN: Common-Feature-Learning-Based Context-Aware Generative Adversarial Network for Glioma Grading. Paper presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention.
  • Isensee, F., P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein. 2017. Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge. Paper presented at the International MICCAI Brainlesion Workshop, 287–97. Springer, Cham.
  • Isensee, F., J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, and S. Wirkert. 2019. nnU-Net: Self-adapting framework for U-Net-based medical image segmentation. In Bildverarbeitung für die Medizin, 22-22. Wiesbaden: Springer Vieweg.
  • Kamnitsas, K.,. C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker. 2017. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 36:61–78. doi: 10.1016/j.media.2016.10.004.
  • Lapointe, S., A. Perry, and N. A. Butowski. 2018. Primary brain tumours in adults. The Lancet 392 (10145):432–46. doi: 10.1016/S0140-6736(18)30990-5.
  • Liaqat, A., M. A. Khan, J. H. Shah, M. Sharif, M. Yasmin, and S. L. Fernandes. 2018. Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection. Journal of Mechanics in Medicine and Biology 18 (04):1850038. doi: 10.1142/S0219519418500380.
  • Lindsey, T., and J.-J. Lee. 2020. Automated Cardiovascular pathology assessment using semantic segmentation and ensemble learning. Journal of Digital Imaging 33 (3):607–6. doi: 10.1007/s10278-019-00197-0.
  • Maas, A. L., A. Y. Hannun, and A. Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. Paper presented at the Proc. icml.
  • Menze, B. H., A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, et al. 2015. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging 34 (10) :1993–2024. doi: 10.1109/TMI.2014.2377694.
  • Pei, L., S. Bakas, A. Vossough, S. M. S. Reza, C. Davatzikos, and K. M. Iftekharuddin. 2020. Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion. Biomedical Signal Processing and Control 55:101648. doi: 10.1016/j.bspc.2019.101648.
  • Ronneberger, O., P. Fischer, and T. Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. Paper Presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention.
  • Tiwari, A., S. Srivastava, and M. Pant. 2020. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters 131:244–60. doi: 10.1016/j.patrec.2019.11.020.
  • Upadhyay, N., and A. D. Waldman. 2011. Conventional MRI evaluation of gliomas. The British Journal of Radiology 84 (special_issue_2):S107–S11. doi: 10.1259/bjr/65711810.
  • van Dellen, E., L. Douw, A. Hillebrand, I. H. M. Ris-Hilgersom, M. M. Schoonheim, J. C. Baayen, P. C. De Witt Hamer, D. N. Velis, M. Klein, J. J. Heimans, et al. 2012. MEG network differences between low- and high-grade glioma related to epilepsy and cognition . PloS One 7 (11):e50122 doi: 10.1371/journal.pone.0050122.
  • Weninger, L., O. Rippel, S. Koppers, and D. Merhof. 2018. Segmentation of brain tumors and patient survival prediction: methods for the BraTS 2018 challenge. Paper presented at the International MICCAI Brainlesion Workshop.
  • Xie, L., L. E. M. Wisse, S. R. Das, H. Wang, D. A. Wolk, J. V. Manjòn, and P. A. Yushkevich. 2016. Accounting for the confound of meninges in segmenting entorhinal and perirhinal cortices in T1-weighted MRI. Paper presented at the International Conference on Medical Image Computing and Computer-assisted Intervention, 564–71. Springer, Cham.
  • Yamashita, R., M. Nishio, R. K. G. Do, and K. Togashi. 2018. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9 (4):611–29. doi: 10.1007/s13244-018-0639-9.
  • Yang, T., Y. Ou, and T. Huang. 2017. Automatic segmentation of brain tumor from MR images using SegNet: selection of training data sets. Paper presented at the Proc. 6th MICCAI BraTS Challenge.
  • Zhao, X., Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan. 2018. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis 43:98–111. doi: 10.1016/j.media.2017.10.002.
  • Zhou, C., C. Ding, X. Wang, Z. Lu, and D. Tao. 2020. One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. IEEE Transactions on Image Processing 29:4516–29. doi: 10.1109/TIP.2020.2973510.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.