BRAIN TUMOR SEGMENTATION ON FLAIR MR IMAGES WITH U-NET

Öz

Brain tumors are among the illnesses that, if not treated promptly, can lead to death. It is extremely difficult to detect tumor tissue using only eye examination methods. As a result, Magnetic Resonance (MR) imaging is used to diagnose brain tumors. T1, T1c, T2, and FLAIR MRI sequences provide detailed information about brain tumors. If the segmentation procedure is performed correctly, patients' chances of survival improve. This paper describes an automated brain tumor segmentation for FLAIR sequences in MR images using U-NeT method. The study has been carried out on the BraTS 2018 data set. The models' correctness has been assessed using the binary accuracy, dice coefficient, and IOU assessment criteria. The results of the comparison between the tumor regions identified by the expert physicians and the tumor regions calculated by the U-Net model are as follows: The model has been completed with 99.26% accuracy, and the dice coefficient value, which expresses the similarity on the basis of pixels for the test data, has been found to be 73.99%. Furthermore, the IOU value of 0.59 demonstrated that the model provided accurate estimates for the study.

Anahtar Kelimeler

• Brain Tumors
• Deep Learning
• U-Net
• Segmentation

Kaynakça

1. Chen, H., Qin, Z., Ding, Y., and Qin Z., "Brain tumor segmentation with deep convolutional symmetric neural network", Neurocomputing, vol. 392, pp. 305-313, 2020.
2. Lorenzo, P. R., Nalepa, J., Billewicz, B. B., Wawrzyniak, P., et al. “ Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks”, Computer Methods and Programs in Biomedicine, vol. 176, pp. 135-148, 2019.
3. Daimary, D., Bora, M. B., Amitab, K., and Kandar, D., “Brain Tumor Segmentation from MRI Images using Hybrid Convolutional Neural Networks”, International Conference on Computational Intelligence and Data Science, 2019.
4. Zeineldin, R. A., Karar, M. E., Coburger, J., Wirtz, C. R., and Burgert, O., “DeepSeg: deep neural network framework for automatic brain tumor”, International Journal of Computer Assisted Radiology and Surgery, vol. 15, pp. 909-920, 2020.
5. Wadhwa, A., Bhardwaj, A., and Verma, V. S., “A review on brain tumor segmentation of MRI images”, Magnetic Resonance Imaging, vol. 61, pp. 247–259, 2019.
6. Sheela, C. J. J., and Suganthi, G., “Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization”, Journal of King Saud University – Computer and Information Sciences, pp. 1-10, 2019.
7. Kalaivani, I., Oliver, A. S., Pugalenthi, R., Jeipratha, P. N., Jeena, A. A. S., and Saranya, G., “Brain Tumor Segmentation Using Machine Learning Classifier”, Fifth International Conference on Science Technology Engineering and Mathematics, 2019.
8. Shehab, L. H., Fahmy, O. M., Gasser, S. M., and El-Mahallawy, M. S., “An efficient brain tumor image segmentation based on deep residual networks (ResNets)”, Journal of King Saud University – Engineering Sciences, 2020.

9. Alqazzaz, S., Sun, X., Yang, X., and Nokes, L., “Automated brain tumor segmentation on multi-modal MR image using SegNet”, Computational Visual Media, vol. 5(2), pp. 209–219, 2019.
10. Dong, H., Yang, G., Liu, F., Mo, Y., and Guo, Y., “Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks”, Medical Image Understanding and Analysis, 2017.
11. Li, S., Tso, G. K., and He, K., “Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation”, Expert Systems with Applications, vol. 145, pp. 1-11, 2020.
12. Zhang, Y., Zhang, E., and Chen, W., “Deep neural network for halftone image classification based on sparse auto-encoder”, Engineering Applications of Artificial Intelligence, vol. 50, pp. 245-255, 2016.
13. Liu, T., Li, Z., Yu, C., and Qin, Y., “NIRS feature extraction based on deep auto-encoder neural network”, Infrared Physics & Technology, vol. 87, pp. 124-128, 2017.
14. Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., and Chen, X., “A sparse auto-encoder-based deep neural network approach for induction motor faults classification”, Measurement, vol. 89, pp. 171-178, 2016.
15. Ronneberger, O., Fischer, P., and Brox, T., “U-Net: Convolutional Networks for Biomedical Image Segmentation”, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234-241, Munich, 2015.
16. F. Isensee, P. Kickingereder, W. Wick, M. Bendszus and K. H. Maier-Hein, “No New-NET”, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, no. 11384, pp. 234-244, 2019.
17. E. Hussain, H. Mahmudul, M. Anisur Rahman, I. Lee, T. Tamanna and M. Zavid Parvez, “CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images”, Chaos, Solutions & Fractals, vol. 142, pp. 1-12, 2021.
18. K. Eckle and J. Schmidt-Hieber, “A comparison of deep networks with ReLU activation function and linear spline-type methods”, Neural Networks, vol. 110, pp. 232-242, 2019.
19. J. Cao, Y. Pang, X. Li and J. Liang, “Randomly translational activation inspired by the input distributions of ReLU”, Neurocomputing, vol. 275, pp. 859-868, 2018.
20. J. Brownlee, “A Gentle Introduction to the Rectified Linear Unit (ReLU)”, 2019. [Online]. Available: https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/. [Accessed 25 8 2022].
21. G. Ser and C. T. Bati, “Determining the Best Model with Deep Neural Networks: Keras Application on Mushroom Data”, YYU Journal of Agricultural Science, vol. 29, no. 3, pp. 406-417, 2019.
22. D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization”, in International Conference on Learning Representations, 2015.
23. S. Remya and R. Sasikala, “Performance evaluation of optimized and adaptive neuro fuzzy inference system for predictive modeling in agriculture,” Computers & Electrical Engineering, vol. 86, pp. 1-14, 2020.
24. Z. Fei, Z. Wu, Y. Xiao, J. Ma and W. He, “A new short-arc fitting method with high precision using Adam optimization algorithm”, Optik, vol. 212, pp. 1-7, 2020.
25. W. Siguerdidjane, F. Khameneifar and F. P. Gosselin, “Efficient planning of peen-forming patterns via artificial neural networks”, Manufacturing Letters, vol. 25, pp. 70-74, 2020.
26. G. Dommaraju, “Keras’ Accuracy Metrics. Understand them by running simple experiments in Python,” [Online]. Available: https://towardsdatascience.com/ keras-accuracy-metrics-8572eb479ec7. [Accessed 15 11 2022].
27. H. Kumar, S. V. DeSouza and M. S. Petrow, “Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review”, Computer Methods and Programs in Biomedicine, vol. 178, pp. 319-328, 2019.
28. M. S. ul Islam, “Using deep learning based methods to classify salt bodies in seismic images”, Journal of Applied Geophysics, vol. 178, pp. 1-9, 2020.
29. S. Bakas, H. Akbari, A. Sotiras, M. Bilello and M. Rozycki, “Segmentation labels and radiomic features for the preoperative scans of the tcga-gbm collection”, Cancer Imaging Arc., vol. 286, 2017.
30. S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki and J. a. Kirby, “Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features”, Nature Scientific Data, 2017.
31. S. Bakas, H. Akbari, A. Sotiras, M. Bilello and e. al., “Segmentation labels and radiomic features for the preoperative scans of the tcga-lgg collection”, The Cancer Imaging Archive, 2017. [Online].
32. S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi and e. al., “Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge,” arXiv:1811.02629, 2018.
33. B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani and J. Kirby, “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)”, IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, 2015.
34. F. Demir, Y. Akbulut, B. Taşcı and K. Demir, “Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data”, Biomedical Signal Processing and Control, vol.81, 2023.
35. H. Mzoughi, I. Njeh, A. Wali, M. B. Slima, A. B. Hamida, C. Mhiri and K. B. Mahfoudhe, “Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification”, Journal of Digital Imaging, vol. 33, pp. 903–915, 2020.
36. K. R. Pedada, R. A. Bhujanga, K. K. Patro, J. P. Allam, M. M. Jamjoom, N. A. Samee, “ A novel approach for brain tumour detection using deep learning based technique”, Biomedical Signal Processing and Control, vol. 82, 2023.
37. G., Ramasamy, T. Singh, X. Yuan, “Multi-Modal Semantic Segmentation Model using Encoder Based Link-Net Architecture for BraTS 2020 Challenge”, Procedia Computer Science, vol. 218, pp. 732-740, 2023.
38. R. A. Zeineldin, M. E. Karar, J. Coburger, C. R. Wirtz, O. Burgert, “DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images”, International Journal of Computer Assisted Radiology and Surgery, vol. 15, pp. 909-920, 2020.
39. W. M. Jwaid, Z. S. Al-Husseini, A. H. Sabry, “Development of Brain Tumor Segmentation of Magnetic Resonance Imaging (MRI) Using U-Net Deep Learning”, Eastern-European Journal of Enterprise Technologies, vol. 4(9), pp. 23-31, 2021.