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Analysis of Detection of Glioma by Segmentation of Brain Tumor MRI Images Using Deep Learning

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Data Science and Applications (ICDSA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 820))

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Abstract

Segmenting a brain tumor is a crucial step in medical image processing. Early detection of brain tumors is crucial for increasing the effectiveness of treatment options and patient survival. To manually diagnose cancer out of brain tumor images is not only challenging but also time-consuming as there are large amounts of MRI images that have to be scanned daily in clinical practice. Therefore, automatic brain tumor picture segmentation would be a great help. An overview of MRI-based brain tumor segmentation techniques is presented in this chapter. Deep learning techniques due to their edge on the conventional techniques have become popular in the field of automatic segmentation due to their superior findings. The massive volumes of MRI-based image data can also be processed effectively and evaluated objectively using deep learning techniques. Numerous review articles that concentrate on conventional techniques for MRI-based brain tumor picture segmentation are already available. Unlike other papers, this one concentrates on the current deep learning approaches trend in this area. First, a brief overview of brain tumors and techniques for segmenting them is provided. The most current developments in deep learning algorithms are then reviewed, along with state-of-the-art algorithms. Finally, an evaluation of the existing situation is provided, where these brain tumor segmentation techniques based on MRI images could be standardized and implemented into regular clinical practice.

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References

  1. L.M. DeAngelis, Brain tumors. N. Engl. J. Med. 344(2), 114–123 (2001). https://doi.org/10.1056/NEJM200101113440207

    Article  Google Scholar 

  2. A. Deimling, Gliomas. Recent Results in Cancer Research (Springer, 2009), p. 171

    Google Scholar 

  3. R. Stupp, Malignant. Glioma Ann Oncol 18(Suppl. 2), 69–70 (2007)

    Article  Google Scholar 

  4. A. Drevelegas, N. Papanikolou, Imaging Modalities in Brain Tumors Imaging of Brain Tumors with Histological Correlations (Chap 2) (Springer, 2011), pp. 13–34

    Google Scholar 

  5. B.H. Menze, A. Jakab, S. Bauer, J.C. Kalpathy, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)

    Article  Google Scholar 

  6. D.R. White, A.S. Houston, W.F. Sampson, G.P. Wilkins, Intra and interoperator variations in region-of-interest drawing and their effect on the measurement of glomerular filtration rates. Clin. Nucl. Med. 24(3), 177–181 (1999). https://doi.org/10.1097/00003072-199903000-00008

    Article  Google Scholar 

  7. J.L. Foo, A survey of user interaction and automation in medical image segmentation methods. Technical report ISUHCI20062, Human Computer Interaction Department, Iowa State University (2006)

    Google Scholar 

  8. A. Hamamci, N. Kucuk, K. Karaman, K. Engin, G. Unal, Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging 31(3), 790–804 (2012). https://doi.org/10.1109/TMI.2011.2181857

    Article  Google Scholar 

  9. M. Prastawa, E. Bullitt, G. Gerig, Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13(2), 297–311 (2009). https://doi.org/10.1016/j.media.2008.11.002

    Article  Google Scholar 

  10. N. Gordillo, E. Montseny, P. Sobrevilla, State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013). https://doi.org/10.1016/j.mri.2013.05.002

    Article  Google Scholar 

  11. S. Bauer, R. Wiest, L.P. Nolte, M. Reyes, A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97-129 (2013). https://doi.org/10.1088/0031-9155/58/13/R97

    Article  Google Scholar 

  12. J. Liu, J. Wang, F. Wu, T. Liu, Y. Pan, Y. Pan, A survey of MRI- based brain tumor segmentation methods. Tsinghua Sci. Technol. 19(6), 578–595 (2014). https://doi.org/10.1109/TST.2014.6961028

  13. E.D. Angelini, O. Clatz, Glioma dynamics and computational models: a review of segmentation, registration, silico growth algorithms and their clinical applications. Curr. J. Med. Imaging 3, 262–276 (2007)

    Article  Google Scholar 

  14. D. Kwon et al., in Medical Image Computing and Computer-Assisted Intervention—MICCAI. Combining Generative Models for Multifocal Glioma Segmentation and Registration (Springer, 2014), pp. 763–770.

    Google Scholar 

  15. R.D. Nowak, Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Process. 8(10), 1408–1419 (1999). https://doi.org/10.1109/83.791966

    Article  Google Scholar 

  16. A.H. Zhuang, D.J. Valentino, A.W. Toga, Skull stripping magnetic resonance brain images using a model based level set. Neuroimage 32(1), 79–92 (2006). https://doi.org/10.1016/j.neuroimage.2006.03.019

    Article  Google Scholar 

  17. M. Shah, Y. Xiao, N. Subbanna, S. Francis, D.L. Arnold, D.L. Collins, T. Arbel, Evaluating intensity normalization on MRI’s of human brain with multiple sclerosis. Med. Image Anal. 15(2), 267–282 (2011). https://doi.org/10.1016/j.media.2010.12.003

    Article  Google Scholar 

  18. N. Tustison et al., Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTSR. Neuroinformatics 13(2), 209–222 (2015)

    Article  Google Scholar 

  19. V. Anitha, S. Murugavalli, Brain tumor classification using two-tier classifier with adaptive segmentation technique. IET Comput. Vision 10(1), 9–17 (2016). https://doi.org/10.1049/iet-cvi.2014.0193

    Article  Google Scholar 

  20. T. Leung, J. Malik, Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vision 43(1), 29–44 (2001). https://doi.org/10.1023/A:1011126920638

    Article  Google Scholar 

  21. A. Islam, S.M. Reza, K.M. Iftekharuddin, Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans. Bio-Med. Eng. 60(11), 3204–3215 (2013). https://doi.org/10.1109/TBME.2013.2271383

  22. S. Bauer, L.P. Nolte, M. Reyes, Medical Image Computing and Computer-Assisted Intervention—MICCAI. Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization (Springer, 2011), pp. 354–361

    Google Scholar 

  23. D. Zikic et al., Decision forests for tissue-specific segmentation of high- grade gliomas in multi-channel MR. Med. Image Comput. Comput. Assist. Interv. 15(3), 369–376 (2012)

    Google Scholar 

  24. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 60(6), pp. 1097–1105 (2017)

    Google Scholar 

  25. D. Ciresan, A. Giusti, L. Gambardella, and J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images. Advances in neural information processing systems, 25, pp. 2843–2851 (2012)

    Google Scholar 

  26. A. Mehmood, A. Galimzianova, A. Hoogi, et al. Deep Learning for Brain Tumor Segmentation: State-of-the-Art and Future Directions. International Conference on Computer and Communication Technologies, Finland, (2021), 449–459

    Google Scholar 

  27. H. Dang, G. Wang, F. Liu, et al. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks. Proceedings of Communications in Computer and Information Science, Malaysia (2021).

    Google Scholar 

  28. J. Nalepa, N. Baid, P.R. Lorenzo, W. Dudzik, et al. Deep Learning-Based Automatic Glioma Segmentation in Multi-Modal MRI Volumes Using a Cascade of Convolutional Neural Networks. Journal of Computer & Electronics Science, 11384 (2021).

    Google Scholar 

  29. Z. Liu, L. Tong, L. Chen, Q. Zhang et al. Deep Learning for Glioma Segmentation: A Comprehensive Review. Proceedings of International Conference on Intelligent Syst. China, 1001–1026 (2021).

    Google Scholar 

  30. J. Ma, R. Ranjbarzadeh, A.B. Kasgari, et al. Glioma Segmentation and Survival Prediction Using Cascaded UNet with Attention Mechanism. Science Research Journal. 11(1) (2020).

    Google Scholar 

  31. J. Dong, Y. Zhang, Y. Meng, T. Yang, W. Ma, H. Yu. Glioma Segmentation in MRI Images Based on Deep Learning with DenseNet, International Journal on Advance Artificial Intelligence, 6(1), pp. 937–43 (2020).

    Google Scholar 

  32. A. Wang, B.K. Kalejahi, S. Meshgini et al. “Glioma Segmentation in MRI Using Fully Convolutional Neural Networks with Adversarial Training”. Proceedings of International Conference on Intelligent and Smart systems, Turkey (2020).

    Google Scholar 

  33. L. Zhang, K. Jia. Multi-Scale Deep Convolutional Neural Network for Brain Tumor Segmentation, International conference on Autonomous Systems, Japan (2020).

    Google Scholar 

  34. M. Isensee , X. He, W. Xu et al. Brain Tumor Segmentation Using Deep Neural Networks Based on Multi-Scale Convolutional Features. International Conference on Neural Network based Smart Sytems., China (2020).

    Google Scholar 

  35. M. Havaei, H. Larochelle, P. Poulin, et al. Within-brain classification for brain tumor segmentation. Int J CARS 11, 777–788 (2016). https://doi.org/10.1007/s11548-015-1311-1

    Article  Google Scholar 

  36. G. Urban et al., in MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). Multi-Modal Brain Tumor Segmentation Using Deep Convolutional Neural Networks (2014), pp. 31–35

    Google Scholar 

  37. D. Zikic et al., in MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). Segmentation of Brain Tumor Tissues with Convolutional Neural Networks (2014), pp. 36–39

    Google Scholar 

  38. M. Havaei, A. Davy, W.D. Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jadoin, H. Larochelle, Brain tumor segmentation with deep neural networks. Med. Image Anal. (2016). https://doi.org/10.1016/j.media.2016.05.004

    Article  Google Scholar 

  39. A. Davy et al., in MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). Brain Tumor Segmentation with Deep Neural Networks (2014), pp. 1–5

    Google Scholar 

  40. S. Pereira, A. Pinto, V. Alves, C.A. Silva, Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016). https://doi.org/10.1109/TMI.2016.2538465

    Article  Google Scholar 

  41. P. Dvorak, B. Menze, in MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). Structured Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation. (2015), pp. 13–24

    Google Scholar 

  42. V. Rao, M.S. Sarabi, A. Jaiswal, in MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). Brain Tumor Segmentation with Deep Learning (2015), pp. 56–59

    Google Scholar 

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Author information

Authors and Affiliations

  1. Chandigarh University, Mohali, India

    Ishani Rana & Paurav Goel

Authors
  1. Ishani Rana
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  2. Paurav Goel
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Corresponding author

Correspondence to Ishani Rana .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Satyasai Jagannath Nanda

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajendra Prasad Yadav

  3. Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  4. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

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Rana, I., Goel, P. (2024). Analysis of Detection of Glioma by Segmentation of Brain Tumor MRI Images Using Deep Learning. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 820. Springer, Singapore. https://doi.org/10.1007/978-981-99-7817-5_20

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