Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.238957

Keywords:

Magnetic Resonance Imaging (MRI), deep learning, Convolutional Neural Network (CNN), 3D U-Net architecture, brain tumors, segmentations

Abstract

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

Author Biographies

Wasan M. Jwaid, University of Thi-Qar

Assistant Teacher

Department of Banking and Finance Administration and Economics

Zainab Shaker Matar Al-Husseini, Imam Ja'afar Al-Sadiq University

Assistant Teacher

Department of Computer Technology Engineering

College of Information Technology

Ahmad H. Sabry, Universiti Tenaga Nasional

Doctor of Control and Automation Engineering

Department of Institute of Sustainable Energy

References

  1. Zwanenburg, J. J. M., Hendrikse, J., Visser, F., Takahara, T., Luijten, P. R. (2010). Fluid attenuated inversion recovery (FLAIR) MRI at 7.0 Tesla: comparison with 1.5 and 3.0 Tesla. European Radiology, 20 (4), 915–922. doi: https://doi.org/10.1007/s00330-009-1620-2
  2. Zeineldin, R. A., Karar, M. E., Coburger, J., Wirtz, C. R., Burgert, O. (2020). DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. International Journal of Computer Assisted Radiology and Surgery, 15 (6), 909–920. doi: https://doi.org/10.1007/s11548-020-02186-z
  3. Sun, L., Zhang, S., Chen, H., Luo, L. (2019). Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning. Frontiers in Neuroscience, 13. doi: https://doi.org/10.3389/fnins.2019.00810
  4. Kulkarni, S. M., Sundari, G. (2020). A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm. International Journal of Advanced Computer Science and Applications, 11 (8). doi: https://doi.org/10.14569/ijacsa.2020.0110848
  5. Al-qazzaz Salma, Sun, X., Yang, H., Yang, Y., Xu, R., Nokes, L., Yang, X. (2020). Image classification-based brain tumour tissue segmentation. Multimedia Tools and Applications, 80 (1), 993–1008. doi: https://doi.org/10.1007/s11042-020-09661-4
  6. Cui, S., Mao, L., Jiang, J., Liu, C., Xiong, S. (2018). Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network. Journal of Healthcare Engineering, 2018, 1–14. doi: https://doi.org/10.1155/2018/4940593
  7. Mlynarski, P., Delingette, H., Criminisi, A., Ayache, N. (2019). 3D convolutional neural networks for tumor segmentation using long-range 2D context. Computerized Medical Imaging and Graphics, 73, 60–72. doi: https://doi.org/10.1016/j.compmedimag.2019.02.001
  8. Ruba, T., Tamilselvi, R., Beham, M. P., Aparna, N. (2020). Accurate Classification and Detection of Brain Cancer Cells in MRI and CT Images using Nano Contrast Agents. Biomedical and Pharmacology Journal, 13 (03), 1227–1237. doi: https://doi.org/10.13005/bpj/1991
  9. Sun, J., Chen, W., Peng, S., Liu, B. (2019). DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation. Journal of Medical Systems, 43 (7). doi: https://doi.org/10.1007/s10916-019-1358-6
  10. Sajid, S., Hussain, S., Sarwar, A. (2019). Brain Tumor Detection and Segmentation in MR Images Using Deep Learning. Arabian Journal for Science and Engineering, 44 (11), 9249–9261. doi: https://doi.org/10.1007/s13369-019-03967-8
  11. Farahani, A., Mohseni, H. (2020). Medical image segmentation using customized U-Net with adaptive activation functions. Neural Computing and Applications, 33 (11), 6307–6323. doi: https://doi.org/10.1007/s00521-020-05396-3
  12. Naser, M. A., Deen, M. J. (2020). Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Computers in Biology and Medicine, 121, 103758. doi: https://doi.org/10.1016/j.compbiomed.2020.103758
  13. Abdelhafiz, D., Bi, J., Ammar, R., Yang, C., Nabavi, S. (2020). Convolutional neural network for automated mass segmentation in mammography. BMC Bioinformatics, 21 (S1). doi: https://doi.org/10.1186/s12859-020-3521-y
  14. Rao, S., Lingappa, B. (2019). Image Analysis for MRI Based Brain Tumour Detection Using Hybrid Segmentation and Deep Learning Classification Technique. International Journal of Intelligent Engineering and Systems, 12 (5), 53–62. doi: https://doi.org/10.22266/ijies2019.1031.06
  15. Yuvaraj, D., Noori, S. F., Swaminathan, S. (2021). Multi-perspective scaling convolutional neural networks for high-resolution MRI brain image segmentation. Materials Today: Proceedings. doi: https://doi.org/10.1016/j.matpr.2020.12.199
  16. Mzoughi, H., Njeh, I., Slima, M. B., Ben Hamida, A., Mhiri, C., Mahfoudh, K. B. (2020). Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures. Multimedia Tools and Applications, 80 (1), 899–919. doi: https://doi.org/10.1007/s11042-020-09786-6
  17. Rajasree, R., Columbus, C. C., Shilaja, C. (2020). Multiscale-based multimodal image classification of brain tumor using deep learning method. Neural Computing and Applications, 33 (11), 5543–5553. doi: https://doi.org/10.1007/s00521-020-05332-5
  18. Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234–241. doi: https://doi.org/10.1007/978-3-319-24574-4_28

Downloads

Published

2021-08-31

How to Cite

Jwaid, W. M., Al-Husseini, Z. S. M., & Sabry, A. H. . (2021). Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning. Eastern-European Journal of Enterprise Technologies, 4(9(112), 23–31. https://doi.org/10.15587/1729-4061.2021.238957

Issue

Section

Information and controlling system