110
Views
0
CrossRef citations to date
0
Altmetric
Computers and computing

An Efficient Encoder-Decoder CNN for Brain Tumor Segmentation in MRI Images

&

References

  • P. L. Chithra and G. Dheepa, “Di-phase midway convolution and deconvolution network for brain tumor segmentation in MRI images,” International Journal of Imaging Systems and Technology, Vol. 30, no. 3, pp. 674–86, 2020.
  • S. Pereira and V. Alves, “Brain tumor segmentation using convolutional neural network in MRI images,” IEEE Trans. Med. Imaging, Vol. 35, no. 5, pp. 1240–51, 2016.
  • J. Juan-Albarracín and F. Garcia, “Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification,” PLOS One, Vol. 10, no. 5, pp. 1–20, 2016.
  • M. V. Anitha and R. Pallikonda, “An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain image,” Appl. Soft Comput., Vol. 57, pp. 399–426, 2017.
  • B. H. Menze and A. Jakab, “Multimodal brain tumor image segmentation benchmarks,” IEEE Trans. Med. Imaging, Vol. 34, no. 10, pp. 1993–2024, 2015.
  • M. Angulakshmi and G. Lakshmi Priya, “Brain tumour segmentation from MRI using superpixels based spectral clustering,” J. King Saud Univ. Comput. Inf. Sci., Vol. 32, no. 10, pp. 1182–1193, 2018.
  • R. Meier1 and B. Stefan, “A hybrid model for multimodal brain tumor segmentation,” in Proceedings of NCI MICCAI BRATS 2013, 1, 31, 2013.
  • J. Festa and J. Mariz, “Automatic brain tumor segmentation of multi-sequence MRI images using random decision forests,” in Proceedings MICCAI BRATS, 2013.
  • P. L. Chithra and G. Dheepa, “An analysis of segmenting and classifying tumor regions in MRI images using CNN,” Int. J. Pure Appl. Math., Vol. 118, no. 24, pp. 1–12, 2018.
  • S. Li and Z. Songtao, “Multi-view 3D CNN with dense CRF for brain tumor segmentation and survival prediction,” in MICCAI BRATS 2018, pp. 448–56, 2018.
  • M. Havaei and A. Davy, “Brain tumor segmentation with deep neural network,” Med. Image Anal., Vol. 35, pp. 18–31, 2017.
  • Z. Xiaomei, et al., “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation,” Med. Image Anal., Vol. 43, pp. 98–111, 2018.
  • M. Michal and N. Jakub, “Automatic Brain Tumor Segmentation Using a Two-Stage Multi-Modal FCNN,” in MICCAI BRATS 2018, pp. 314–21, 2018.
  • J. Kleesiek and G. Urban, “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping,” Neuro Image, Vol. 129, pp. 460–9, 2016.
  • C. Eric and Chang. “Automatic brain tumor segmentation using a U-net neural network,” in MICCAI BRATS 2018, 2018, pp. 63–73.
  • Y. Robail, et al., “ECRU: An encoder-decoder based convolution neural network (CNN) for road-scene understanding,” J. Imaging, Vol. 4, no. 116, pp. 1–19, 2018.
  • S. Bauer, et al., “A survey of MRI-based medical image analysis for brain tumor studies,” Phys. Med. Biol, Vol. 58, no. 13, pp. 97–129, 2013.
  • L. Jin and L. Min, “A survey of MRI-based brain tumor segmentation methods,” Tsinghua. Sci. Technol., Vol. 19, no. 6, pp. 578–95, 2014.
  • S. M. Kamrul Hasan and M. Ahmad, “Two-step verification of brain tumor segmentation using watershed matching algorithm,” Brain Inform., Vol. 5, p. 8, 2018.
  • G. B. Praveen, “Multi stage classification and segmentation of brain tumor,” IEEE, Vol. 9, pp. 1628–32, 2016.
  • W. Yao and T. Yang. “Semi-automatic segmentation of brain tumors using population and individual information,” J Digit Imaging, Vol. 26, pp. 786–96, 2013.
  • K. E. Emblem and B. Nedregaard, “Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge-based fuzzy clustering,” J. Magn. Reson. Imaging, Vol. 30, no. 1, pp. 1–10, 2009.
  • K. D. Yogita and M. M. Miind, “Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering,” Biocybern. Biomed. Eng. Vol. 36, pp. 413–26, 2017.
  • N. Sauwen and M. Acou, “Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI,” NeuroImage Clin., Vol. 12, pp. 753–64, 2016.
  • T. R. Raviv and K. V. Leemput, “Multi-modal brain tumor segmentation via latent atlases,” in Proceedings MICCAI BRATS, 2012.
  • R. Meier, et al., “Appearance-and context-sensitive features for brain tumor segmentation,” in MICCAI BRATS, 2014, pp. 20–26.
  • L. Zhao and D. Sarikaya, “Automatic brain tumor segmentation with MRF on supervoxels,” in Proceedings MICCAI BRATS 2012.
  • S. Bauer and T. Fejes, “Segmentation of brain tumor images based on integrated hierarchical classification and regularization,” in Proceedings BRATS 2012.
  • U. Khalid and R. Kashif. Brain tumor classification from multi-modality MRI using wavelets and machine learning, Vol. 20, 2017, pp. 871–81.
  • N. Varuna Shree and T. N. R. Kumar, “Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network,” Brain. Inform., Vol. 5, pp. 23–30, 2018.
  • N. Nooshin and K. Miroslav, “Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical feature,” Comput. Electr. Eng., Vol. 45, pp. 286–301, 2015.
  • D. Zikic and Y. Ioannou, “Segmentation of brain tumor tissues with convolutional neural networks,” Proceedings MICCAI BraTS, 2014, pp. 36–39.
  • Z. Akkus and A. Galimzianova, “Deep learning for brain MRI segmentation: state of the art and feature directions,” J. Digit Imaging, Vol. 30, pp. 449–59, 2017.
  • V. Anitha and S. Murugavalli, “Brain tumour classification using two-tier classifier with adaptive segmentation technique,” IET Comput. Vision, Vol. 10, no. 1, pp. 9–17, 2018.
  • S. Dieleman and K. W. Willett, “Rotation-invariant convolutional neural networks for galaxy morphology prediction,” Monthly Notices R. Astronom. Soc., Vol. 450, no. 2, pp. 1441–59, 2015.
  • Y. Bengio and A. Courville, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 35, no. 8, pp. 1798–828, 2013.
  • R. Atique, et al., End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography. Computer Vision – ECCV 2018 Workshops, Springer Nature, 2019, pp. 723–9.
  • H. Dong and G. Ysng, “Automatic brain tumor detection and segmentation using U-net based fully convolutional network,” Comput. Vis. Pattern Recog., Vol. 69, no. 3, pp. 1–12, 2017.
  • N. J. Tustison and B. B. Avants, ““N4ITK: Improved N3 bias correction,” IEEE Trans. Med. Imaging, Vol. 29, pp. 1310–20, 2010.
  • A. L. Maas and A. Y. Hannun, “Rectifier nonlinearities improve neural network acoustic models,” In Proc. ICML, Vol. 30, 2013.
  • C. Mariano and V. Sergi, “Survival prediction using ensemble tumor segmentation and transfer learning,” MICCAI BRATS 2018, pp. 54–62, 2018.

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.