EVALUATION OF U-Net AND ResNet ARCHITECTURES FOR BIOMEDICAL IMAGE SEGMENTATION

Öz

Medical professionals need methods that provide reliable information in diagnosing and monitoring neurological diseases. Among such methods, studies based on medical image analysis are essential among the active research topics in this field. Tumor segmentation is a popular area, especially with magnetic resonance imaging (MRI). Early diagnosis of tumours plays an essential role in the treatment process. This situation also increases the survival rate of the patients. Manually segmenting a tumour from MR images is a difficult and time-consuming task within the anatomical knowledge of medical professionals. This has necessitated the need for automatic segmentation methods. Convolutional neural networks (CNN), one of the deep learning methods that provide the most advanced results in the field of tumour segmentation, play an important role. This study, tumor segmentation was performed from brain and heart MR images using CNN-based U-Net and ResNet50 deep network architectures. In the segmentation process, their performance was tested using Dice, Sensitivity, PPV and Jaccard metrics. High performance levels were sequentially achieved using the U-Net network architecture on brain images, with success rates of approximately 98.47%, 98.1%, 98.85%, and 96.07%

Anahtar Kelimeler

• Tumor segmentation
• Magnetic resonance imaging
• MRI
• Deep learning
• Glioma

Kaynakça

1. 1. Ostrom, Q. T. et al., “CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014”, Neuro Oncol, Vol. 9, Issue 5, Pages 1– 88, 2017.
2. 2. Soltaninejad, M. et al., “Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI”, Int J Comput Assist Radiol Surg, Vol. 12, Issue 2, Pages 183–203, 2017.
3. 3. Louis, D. N. et al., “The 2007 WHO classification of tumours of the central nervous system”, Acta Neuropathologica, 114, Pages 97–109, 2007.
4. 4. Menze, B. H. et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)”, IEEE Trans Med Imaging, Vol. 34, Issue 10, Pages 1993–2024, 2015.
5. 5. Greenspan, H., Van Ginneken, B. & Summers, R. M., “Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique”, IEEE Transactions on Medical Imaging, Vol. 35, Issue 5, Pages 1153– 1159, 2016.
6. 6. De Brébisson, A. & Montana, G., “Deep neural networks for anatomical brain segmentation”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Boston, Pages 20–28, 2015.
7. 7. Tian, Z., Liu, L., Zhang, Z. & Fei, B., “PSNet: prostate segmentation on MRI based on a convolutional neural network”, Journal of Medical Imaging, Vol. 5, Issue 2, Pages 021208-021208, 2018.
8. 8. Avendi, M. R., Kheradvar, A. & Jafarkhani, H., “A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI”, Med Image Anal, Vol. 30, Pages 108–119, 2016.

9. 9. Bauer, S., Nolte, L. P. & Reyes, M., “Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Toronto, Pages 354-361, 2011.
10. 10. Caldairou, B., Passat, N., Habas, P. A., Studholme, C. & Rousseau, F., “A non-local fuzzy segmentation method: Application to brain MRI”, Pattern Recognition, Vol. 44, Issue 9, Pages 1916-1927, 2011.
11. 11. Zikic, D. et al., “Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Nice, Pages 369-376, 2012.
12. 12. Mathew, A. R. & Anto, P. B., “Tumor detection and classification of MRI brain image using wavelet transform and SVM”, Proceedings of IEEE International Conference on Signal Processing and Communication, ICSPC 2017, Coimbatore Pages 75–78, 2017.
13. 13. Kailash D. Kharat, Pradyumna P. Kulkarni, M. B. N., “Brain Tumor Classification Using Neural Network Based Methods”, International Journal of Computer Science and Informatics, Vol. 1, Issue 2, Pages 2231–5292, 2012.
14. 14. Abdel-Maksoud, E., Elmogy, M. & Al-Awadi, R, “Brain tumor segmentation based on a hybrid clustering technique”, Egyptian Informatics Journal, Vol. 16, Issue 1, Pages 71–81, 2015.
15. 15. Pinto, A., Pereira, S., Rasteiro, D. & Silva, C. A., “Hierarchical brain tumour segmentation using extremely randomized trees”, Pattern Recognit, Vol. 82, Pages 105–117, 2018.
16. 16. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L. & Erickson, B. J., “Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions”, Journal of Digital Imaging, Vol. 30, Pages 449–459, 2017.
17. 17. Wang, S. H., Sun, J., Phillips, P., Zhao, G. & Zhang, Y. D., “Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units”, Journal of Real-Time Image Processing, Vol. 15, Pages 631–642, 2018.
18. 18. Chen, H., Dou, Q., Yu, L., Qin, J. & Heng, P. A., “VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images”, NeuroImage, Vol. 170, Pages 446–455, 2018.
19. 19. Wang, G., Li, W., Ourselin, S. & Vercauteren, T., “Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10670, Pages 178–190, 2018.
20. 20. Diaz, I. et al., “An automatic brain tumor segmentation tool”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaca, Pages 3339-3342, 2013.
21. 21. Liu, J. et al., “A survey of MRI-based brain tumor segmentation methods”, Tsinghua Science and Technology, Vol. 19, Issue 6, Pages 578-595, 2014.
22. 22. Chen, W., Liu, B., Peng, S., Sun, J. & Qiao, X., “S3D-UNET: Separable 3D U-Net for brain tumor segmentation”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11384, Pages 358-368, 2019.
23. 23. Dong, H., Yang, G., Liu, F., Mo, Y. & Guo, Y., “Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks”, Communications in Computer and Information Science, Vol. 723, Pages 506–517, Springer, Cham, 2017.
24. 24. Myronenko, A., “3D MRI brain tumor segmentation using autoencoder regularization”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11384, Pages 311-320, 2019.
25. 25. Lachinov, D., Vasiliev, E. & Turlapov, V., “Glioma segmentation with cascaded UNet”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Cham: Springer International Publishing, Pages 189-198, 2018.
26. 26. Ronneberger, O., Fischer, P. & Brox, T., “U-net: Convolutional networks for biomedical image segmentation”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9351, Pages 234–241, 2015.
27. 27. Antonelli, M. et al., “The Medical Segmentation Decathlon”, Nature Communications, Vol. 13, Issue 1, Pages 1-13, 2022.
28. 28. “Medical Segmentation Decathlon”, http://medicaldecathlon.com/, June 06, 2023.
29. 29. Diederik, K. & Ba, J. L. “ADAM: A Method for Stochastic Optimization”, arXiv preprint arXiv:1412.6980, 2014.
30. 30. Ronneberger O, Fischer P, Brox T., “U-net: Convolutional networks for biomedical image segmentation”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9351, Pages 234–241, 2015.
31. 31. Krizhevsky, A., Sutskever, I. & Hinton, G. E., “ImageNet classification with deep convolutional neural networks”, Commun ACM, Vol. 60, Issue 6, Pages 84-90, 2017.
32. 32. Dice, L. R., “Measures of the Amount of Ecologic Association Between Species”, Ecology, Vol. 26, Issue 3, Pages 297-302, 1945.
33. 33. Li, H., Li, A. & Wang, M., “A novel end-to-end brain tumor segmentation method using improved fully convolutional networks”, Comput Biol Med, Vol. 108, Pages 150-160, 2019.
34. 34. Taha, A. A. & Hanbury, A., “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool”, BMC Med Imaging, Vol. 15, Issue 1, Pages 1-28, 2015.
35. 35. Havaei, M. et al., “Brain tumor segmentation with Deep Neural Networks”, Medical Image Analysis, Vol. 35, Pages 18–31, 2017.
36. 36. Kamnitsas, K. et al., “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation” ,Medical Image Analysis, Vol. 36, Pages 61–78, 2017.
37. 37. Shreyas, V. & Pankajakshan, V., “A deep learning architecture for brain tumor segmentation in MRI images”, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), Luton, Pages 1–6, 2017.
38. 38. Kong, X., Sun, G., Wu, Q., Liu, J. & Lin, F., “Hybrid pyramid u-net model for brain tumor segmentation”, IFIP Advances in Information and Communication Technology, Springer, Cham., Vol. 538, Pages 346–355, 2018. 39. Chen, S., Ding, C. & Liu, M., “Dual-force convolutional neural networks for accurate brain tumor segmentation”, Pattern Recognit, Vol. 88, Pages 90–100, 2019.
39. 40. Tan, L., Ma, W., Xia, J. & Sarker, S., “Multimodal Magnetic Resonance Image Brain Tumor Segmentation Based on ACU-Net Network”, IEEE Access, Vol. 9, Pages 14608–14618, 2021.
40. 41. Kausar, A., Razzak, I., Shapiai, I. & Alshammari, R., “An Improved Dense V-Network for Fast and Precise Segmentation of Left Atrium”, 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, Pages 1-8, 2021.
41. 42. Chakravarty, A. & Sivaswamy, J., “RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation”, IEEE J Biomed Health Inform, Vol. 23, Issue 3, Pages 1151–1162, 2019.