Study of Brain Region Segmentation Using Convolutional Neural Network
Keywords:
Magnetic Resonance Imaging, neuroimaging, convolutional neural network, MICCAI-BRATS challenge.
Abstract
Magnetic Resonance Imaging (MRI) is used in medical imaging for detection of tumours and visualize brain tissues. This is done manually by expert radiologist and this takes good amount of time. The traditional method of MRI evaluation of tumour depends greatly on qualitative features, like density of tumour, growth pattern etc. Brain Region Segmentation is important in neuroimaging application, for example, alignment of images, surface reconstruction etc. The previous methods depends upon the qualitative features and is very sensitive to errors. Noise and errors need to be reduced and efficiently delineated, very less work is done in automatic tumour detection using deep learning methods and there is lot of areas which can be explored. The deep learning method is very much different from the machine learning method. The machine learning method uses algorithms to input data, learn from given data, and make decision based on the experience or learning whereas the deep learning can learn and make decisions on its own. Deep learning has a capability of learning from data that is unstructured or unlabeled. In deep learning, the algorithms try to learn using method of feature extraction which is very different and makes the model fully automatic, here we don’t require any handcrafted feature. In traditional method we need to develop feature extractor for different problem, so we use deep learning which reduces effort of developing different feature extractor for different problem. In one of method of 2-D
References
1. S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1240–1251, May 2016.
2. Alex V, Safwan M, Krishnamurthi G, “Automatic Segmentation and Overall Survival Prediction in Gliomas Using Fully Convolutional Neural Network and Texture Analysis,”. In International MICCAI Brainlesion Workshop 2017 Sep 14 (pp. 216-225).Springer, Cham.
3. Shaikh M, Anand G, Acharya G, Amrutkar A, Alex V, Krishnamurthi G “Brain Tumour Segmentation Using Dense Fully Convolutional Neural Network,” . In International MICCAI Brainlesion Workshop 2017 Sep 14 (pp. 309-219).Springer, Cham.
4. K. Kamnitsas et al., “DeepMedic on Brain Tumor Segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 18–22.
5. M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput, and G. Gerig, “Automatic Brain Tumor ed on iterative selected Segmentation by Subject Specific Modification of Atlas Priors,” Acad. Radiol., vol. 10, no. 12, pp. 1341–1348, Dec. 2003.
6. B. H. Menze et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Trans. Med. Imaging, vol. 34, no. 10, pp. 1993–2024, Oct. 2015.
7. K. Umamaheswari, P. Rajesh, S. S. Rao, and P. V. Babu, “Application of Segmentation Methodology for Extracting MRI Brain Tumor Duly Mitigating the Noise,” in 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp. 288–292.
8. A. Nandi, “Detection of human brain tumour using MRI image segmentation and morphological operators,” in 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), 2015, pp. 55–60.
9. E. F. Badran, E. G. Mahmoud, and N. Hamdy, “An algorithm for detecting brain tumors in MRI images,” in The 2010 International Conference on Computer Engineering Systems, 2010, pp. 368–373.
10. M. Y. Bhanumurthy and K. Anne, “An automated detection and segmentation of tumor in brain MRI using artificial intelligence,” in 2014 IEEE International Conference on Computational Intelligence and Computing Research, 2014, pp. 1–6.
11. M. Arikan, B. Fröhler, and T. Möller, “Semi-Automatic Brain Tumor Segmentation Using Support Vector Machines and Interactive Seed Selection,” Proc. MICCAI-BRATS Workshop 2016, pp. 1–3, 2016.
12. M. F. B. Othman, N. B. Abdullah, and N. F. B. Kamal, “MRI brain classification using support vector machine,” in Simulation and Applied Optimization 2011 Fourth International Conference on Modeling, 2011, pp. 1–4.
13. M. S. Jahanavi and S. Kurup, “A novel approach to detect brain tumour in MRI images using hybrid technique with SVM classifiers,” 2016, pp. 546–549.
14. B. Song, C. Chou, A. Huang, and M. Liu, “Anatomy-Guided Brain Tumor Segmentation and Classification,” Proc. MICCAI-BRATS Workshop 2016, pp. 61–64, 2016.
15. L. Lefkovits, S. Lefkovits, and L. Szil´agyi, “Brain Tumor Segmentation with Optimized Random Forest,” Proc. MICCAI-BRATS Workshop 2016, pp. 30–34, 2016.
16. L. Folgoc, A. Nori, J. Alvarez-Valle, and R. Lowe, “Segmentation of Brain Tumors via Cascades of Lifted Decision Forests,” Proc. MICCAI-BRATS Workshop 2016, pp. 35–39, 2016.
17. A. Ellwaa et al., “Brain Tumor Segmantation using Random Forest trained on iterative selected patients,” Proc. MICCAI-BRATS Workshop 2016, pp. 14–17, 2016.
18. M. Karnan and K. Selvanayaki, “Improved implementation of brain MR image segmentation using Meta heuristic algorithms,” in 2010 IEEE International Conference on Computational Intelligence and Computing Research, 2010, pp. 1–4.
19. A. Sehgal, S. Goel, P. Mangipudi, A. Mehra, and D. Tyagi, “Automatic brain tumor segmentation and extraction in MR images,” 2016, pp. 104–107.
20. Y. B. M and K. Anne, “An automated MRI segmentation framework for brains with tumors and multiple sclerosis lesions,” in 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), 2016, pp. 231–236.
21. C. Sompong and S. Wongthanavasu, “Brain tumor segmentation using cellular automata- based fuzzy c-means,” 2016, pp. 1–6.
22. D. Dera, N. Bouaynaya, and H. Fathallah-Shaykh, “Assessing the Non-Negative Matrix Factorization Level Set Segmentation on the BRATS Benchmark,” Proc. MICCAI-BRATS Workshop 2016, pp. 10–13, 2016.
23. R. Meier, U. Knecht, R. Wiest, and M. Reyes, “CRF-based Brain Tumor Segmentation: Alleviating the Shrinking Bias,” Proc. MICCAI-BRATS Workshop 2016, pp. 44–48, 2016.
24. X. Zhao, Y. Wu, G. Song, Z. Li, Y. Fan, and Y. Zhang, “Brain tumor segmentation using a fully convolutional neural network with conditional random fields,” Proc. MICCAI-BRATS Workshop 2016, pp. 77–80, 2016.
25. D. Zikic, Y. Loannou, M. Brown, and A. Criminisi, “Segmentation of Brain Tumor Tissues with Convolutional Neural Networks,” Proc. MICCAI-BRATS Workshop 2014, pp. 36–39, 2014.
26. P. Dvorak and B. H. Menze, “Structured Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation,” Proc MICCAI-BRATS 2015, pp. 13–24, 2015.
27. M. Havaei, F. Dutil, C. Pal, H. Larochelle, and P. Jodoin, “A Convolutional Neural Network Approach to Brain Tumor Segmentation,” Proc MICCAI-BRATS 2015, pp. 29–33, 2015.
28. R. McKinley, R. Wiest, and M. Reyes, “Nabla-net: a deep dag-like convolutional architecture for biomedical image segmentation: application to high- and low-grade glioma segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 40–43, 2016.
29. P. Chang, “Fully Convolutional Neural Networks with Hyperlocal Features for Brain Tumor Segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 4–8, 2016.
30. T. Lun and W. Hsu, “Brain Tumor Segmentation Using Deep Convolutional Neural Network,” Proc. MICCAI-BRATS Workshop 2016, pp. 26–29, 2016.
31. Z. Cui, J. Yang, and Y. Qiao, “Brain MRI segmentation with patch-based CNN approach,” in 2016 35th Chinese Control Conference (CCC), 2016, pp. 7026–7031.
32. R. Randhawa, A. Modi, P. Jain, and P. Warier, “Improving segment boundary classification for Brain Tumor Segmentation and longitudinal disease progression,” Proc. MICCAI-BRATS Workshop 2016, pp. 53–56, 2016.
33. G. Urban, M. Bendszus, F. Hamprecht, and J. Kleesiek, “Multi-modal Brain Tumor Segmentation using Deep Convolutional Neural Networks,” Proc. MICCAI-BRATS Workshop 2014, pp. 31–35, 2014.
34. B. Pandian, J. Boyle, and D. Orringer, “Multimodal Tumor Segmentation with 3D Volumetric Convolutional Neural Networks,” Proc. MICCAI-BRATS Workshop 2016, pp. 49– 52, 2016.
35. A. Casamitjana, S. Puch, A. Aduriz, E. Sayrol, and V. Vilaplana, “3D Convolutional Networks for Brain Tumor Segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 65–68, 2016.
36. D. Sridhar and I. M. Krishna, “Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network,” in Image Processing Pattern Recognition 2013 International Conference on Signal Processing, 2013, pp. 92–96.
37. E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640–651, Apr. 2017.
38. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.
39. N. Duta and M. Sonka, “Segmentation and interpretation of MR brain images: an improved active shape model,” IEEE Transactions on Medical Imaging, vol. 17, no. 6, pp. 1049–1062, 1998.
40. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Pearson Education, 2008.
41. M. C. Morrone and R. A. Owens, “Feature detection from local energy,” Pattern Recognition Letters, vol. 6, no. 5, pp. 303–313, 1987.
2. Alex V, Safwan M, Krishnamurthi G, “Automatic Segmentation and Overall Survival Prediction in Gliomas Using Fully Convolutional Neural Network and Texture Analysis,”. In International MICCAI Brainlesion Workshop 2017 Sep 14 (pp. 216-225).Springer, Cham.
3. Shaikh M, Anand G, Acharya G, Amrutkar A, Alex V, Krishnamurthi G “Brain Tumour Segmentation Using Dense Fully Convolutional Neural Network,” . In International MICCAI Brainlesion Workshop 2017 Sep 14 (pp. 309-219).Springer, Cham.
4. K. Kamnitsas et al., “DeepMedic on Brain Tumor Segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 18–22.
5. M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput, and G. Gerig, “Automatic Brain Tumor ed on iterative selected Segmentation by Subject Specific Modification of Atlas Priors,” Acad. Radiol., vol. 10, no. 12, pp. 1341–1348, Dec. 2003.
6. B. H. Menze et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Trans. Med. Imaging, vol. 34, no. 10, pp. 1993–2024, Oct. 2015.
7. K. Umamaheswari, P. Rajesh, S. S. Rao, and P. V. Babu, “Application of Segmentation Methodology for Extracting MRI Brain Tumor Duly Mitigating the Noise,” in 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp. 288–292.
8. A. Nandi, “Detection of human brain tumour using MRI image segmentation and morphological operators,” in 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), 2015, pp. 55–60.
9. E. F. Badran, E. G. Mahmoud, and N. Hamdy, “An algorithm for detecting brain tumors in MRI images,” in The 2010 International Conference on Computer Engineering Systems, 2010, pp. 368–373.
10. M. Y. Bhanumurthy and K. Anne, “An automated detection and segmentation of tumor in brain MRI using artificial intelligence,” in 2014 IEEE International Conference on Computational Intelligence and Computing Research, 2014, pp. 1–6.
11. M. Arikan, B. Fröhler, and T. Möller, “Semi-Automatic Brain Tumor Segmentation Using Support Vector Machines and Interactive Seed Selection,” Proc. MICCAI-BRATS Workshop 2016, pp. 1–3, 2016.
12. M. F. B. Othman, N. B. Abdullah, and N. F. B. Kamal, “MRI brain classification using support vector machine,” in Simulation and Applied Optimization 2011 Fourth International Conference on Modeling, 2011, pp. 1–4.
13. M. S. Jahanavi and S. Kurup, “A novel approach to detect brain tumour in MRI images using hybrid technique with SVM classifiers,” 2016, pp. 546–549.
14. B. Song, C. Chou, A. Huang, and M. Liu, “Anatomy-Guided Brain Tumor Segmentation and Classification,” Proc. MICCAI-BRATS Workshop 2016, pp. 61–64, 2016.
15. L. Lefkovits, S. Lefkovits, and L. Szil´agyi, “Brain Tumor Segmentation with Optimized Random Forest,” Proc. MICCAI-BRATS Workshop 2016, pp. 30–34, 2016.
16. L. Folgoc, A. Nori, J. Alvarez-Valle, and R. Lowe, “Segmentation of Brain Tumors via Cascades of Lifted Decision Forests,” Proc. MICCAI-BRATS Workshop 2016, pp. 35–39, 2016.
17. A. Ellwaa et al., “Brain Tumor Segmantation using Random Forest trained on iterative selected patients,” Proc. MICCAI-BRATS Workshop 2016, pp. 14–17, 2016.
18. M. Karnan and K. Selvanayaki, “Improved implementation of brain MR image segmentation using Meta heuristic algorithms,” in 2010 IEEE International Conference on Computational Intelligence and Computing Research, 2010, pp. 1–4.
19. A. Sehgal, S. Goel, P. Mangipudi, A. Mehra, and D. Tyagi, “Automatic brain tumor segmentation and extraction in MR images,” 2016, pp. 104–107.
20. Y. B. M and K. Anne, “An automated MRI segmentation framework for brains with tumors and multiple sclerosis lesions,” in 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), 2016, pp. 231–236.
21. C. Sompong and S. Wongthanavasu, “Brain tumor segmentation using cellular automata- based fuzzy c-means,” 2016, pp. 1–6.
22. D. Dera, N. Bouaynaya, and H. Fathallah-Shaykh, “Assessing the Non-Negative Matrix Factorization Level Set Segmentation on the BRATS Benchmark,” Proc. MICCAI-BRATS Workshop 2016, pp. 10–13, 2016.
23. R. Meier, U. Knecht, R. Wiest, and M. Reyes, “CRF-based Brain Tumor Segmentation: Alleviating the Shrinking Bias,” Proc. MICCAI-BRATS Workshop 2016, pp. 44–48, 2016.
24. X. Zhao, Y. Wu, G. Song, Z. Li, Y. Fan, and Y. Zhang, “Brain tumor segmentation using a fully convolutional neural network with conditional random fields,” Proc. MICCAI-BRATS Workshop 2016, pp. 77–80, 2016.
25. D. Zikic, Y. Loannou, M. Brown, and A. Criminisi, “Segmentation of Brain Tumor Tissues with Convolutional Neural Networks,” Proc. MICCAI-BRATS Workshop 2014, pp. 36–39, 2014.
26. P. Dvorak and B. H. Menze, “Structured Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation,” Proc MICCAI-BRATS 2015, pp. 13–24, 2015.
27. M. Havaei, F. Dutil, C. Pal, H. Larochelle, and P. Jodoin, “A Convolutional Neural Network Approach to Brain Tumor Segmentation,” Proc MICCAI-BRATS 2015, pp. 29–33, 2015.
28. R. McKinley, R. Wiest, and M. Reyes, “Nabla-net: a deep dag-like convolutional architecture for biomedical image segmentation: application to high- and low-grade glioma segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 40–43, 2016.
29. P. Chang, “Fully Convolutional Neural Networks with Hyperlocal Features for Brain Tumor Segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 4–8, 2016.
30. T. Lun and W. Hsu, “Brain Tumor Segmentation Using Deep Convolutional Neural Network,” Proc. MICCAI-BRATS Workshop 2016, pp. 26–29, 2016.
31. Z. Cui, J. Yang, and Y. Qiao, “Brain MRI segmentation with patch-based CNN approach,” in 2016 35th Chinese Control Conference (CCC), 2016, pp. 7026–7031.
32. R. Randhawa, A. Modi, P. Jain, and P. Warier, “Improving segment boundary classification for Brain Tumor Segmentation and longitudinal disease progression,” Proc. MICCAI-BRATS Workshop 2016, pp. 53–56, 2016.
33. G. Urban, M. Bendszus, F. Hamprecht, and J. Kleesiek, “Multi-modal Brain Tumor Segmentation using Deep Convolutional Neural Networks,” Proc. MICCAI-BRATS Workshop 2014, pp. 31–35, 2014.
34. B. Pandian, J. Boyle, and D. Orringer, “Multimodal Tumor Segmentation with 3D Volumetric Convolutional Neural Networks,” Proc. MICCAI-BRATS Workshop 2016, pp. 49– 52, 2016.
35. A. Casamitjana, S. Puch, A. Aduriz, E. Sayrol, and V. Vilaplana, “3D Convolutional Networks for Brain Tumor Segmentation,” Proc. MICCAI-BRATS Workshop 2016, pp. 65–68, 2016.
36. D. Sridhar and I. M. Krishna, “Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network,” in Image Processing Pattern Recognition 2013 International Conference on Signal Processing, 2013, pp. 92–96.
37. E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640–651, Apr. 2017.
38. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.
39. N. Duta and M. Sonka, “Segmentation and interpretation of MR brain images: an improved active shape model,” IEEE Transactions on Medical Imaging, vol. 17, no. 6, pp. 1049–1062, 1998.
40. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Pearson Education, 2008.
41. M. C. Morrone and R. A. Owens, “Feature detection from local energy,” Pattern Recognition Letters, vol. 6, no. 5, pp. 303–313, 1987.
Published
2020-07-28
How to Cite
Sachin Singh. (2020). Study of Brain Region Segmentation Using Convolutional Neural Network. International Journal on Orange Technologies, 2(1), 46-64. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/502
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