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RDCU-Net: A Multi-Scale Residual Dilated Convolution U-Net with Spatial Pyramid Pooling for Brain Tumor Segmentation | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 5، دوره 56، شماره 2، 2024، صفحه 203-212 اصل مقاله (908.3 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2023.22395.5538 | ||
| نویسندگان | ||
| Mohammad Soltani-Gol1؛ Akbar Asgharzadeh-Bonab* 2؛ Hamid Soltanian-Zadeh1؛ Jalil Mazlum3 | ||
| 1Department of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Department of Electrical and Computer Engineering, College of Engineering, Urmia University, Urmia, Iran | ||
| 3Department of Electrical Engineering, Shahid Beheshti Aeronautical University of Science and Technology, Tehran, Iran | ||
| چکیده | ||
| Tumors refer to abnormal growth of cells in the body. Early diagnosis of tumors plays a crucial role in improving treatment conditions , quality of life and patient survival. Deep learning methods are effective for medical image segmentation, but they struggle with tumors in magnetic resonance images (MRI) due to variations in intensity and appearance. Existing models like U-Net face challenges due to the integration of high-level and low-level features, leading to confusion. Our proposed model addresses the above issues by utilizing two techniques and fewer parameters compared to the existing methods, achieving higher accuracy. In the first technique, dilated convolution (DC) blocks with proportional rates are used to integrate high-level and low-level features. The second technique involves selecting dilated spatial pyramid (DSP) blocks, which increase the receptive field of features while maintaining their resolution, contributing to the network's generalization. The proposed model improves training, network depth, and feature extraction by incorporating a residual block. It outperforms the traditional U-Net model in terms of segmentation accuracy and network stability. We evaluated the model using the BraTS 2018 dataset, obtaining Dice coefficients of 0.906, 0.817, and 0.839 for the whole tumor (WT), the enhancing tumor (ET), and the tumor core (TC), respectively. | ||
| کلیدواژه‌ها | ||
| image segmentation؛ deep convolutional neural network؛ magnetic resonance imaging (MRI)؛ brain tumor | ||
| مراجع | ||
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[1] E. G. Van Meir, C. G. Hadjipanayis, A. D. Norden, H. K. Shu, P. Y. Wen, and J. J. Olson, "Exciting new advances in neuro‐oncology: the avenue to a cure for malignant glioma," CA: a cancer journal for clinicians, vol. 60, no. 3, pp. 166-193, 2010.
[2] S. Bakas et al., "Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features," Scientific data, vol. 4, no. 1, pp. 1-13, 2017.
[3]A. Khosravanian, M. Rahmanimanesh, P. Keshavarzi, and S. Mozaffari, "Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method," Computer Methods and Programs in Biomedicine, vol. 198, p. 105809, 2021.
[4] Y. Sim et al., "Deep convolutional neural network–based software improves radiologist detection of malignant lung nodules on chest radiographs," Radiology, vol. 294, no. 1, pp. 199-209, 2020.
[5]O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 2015: Springer, pp. 234-241.
[6] S. Pereira, A. Pinto, V. Alves, and C. A. Silva, "Brain tumor segmentation using convolutional neural networks in MRI images," IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1240-1251, 2016.
[7] F. Milletari, N. Navab, and S.-A. Ahmadi, "V-net: Fully convolutional neural networks for volumetric medical image segmentation," in 2016 fourth international conference on 3D vision (3DV), 2016: Ieee, pp. 565-571.
[8] M. Havaei et al., "Brain tumor segmentation with deep neural networks," Medical image analysis, vol. 35, pp. 18-31, 2017.
[9] T. Yang, J. Song, L. Li, and Q. Tang, "Improving brain tumor segmentation on MRI based on the deep U-net and residual units," Journal of X-ray Science and Technology, vol. 28, no. 1, pp. 95-110, 2020.
[10] M. Noori, A. Bahri, and K. Mohammadi, "Attention-guided version of 2D UNet for automatic brain tumor segmentation," in 2019 9th international conference on computer and knowledge engineering (ICCKE), 2019: IEEE, pp. 269-275.
[11] M. Soltani-Gol, M. Fattahi, H. Soltanian-Zadeh, and S. Sheikhaei, "DRAU-Net: Double Residual Attention Mechanism for automatic MRI brain tumor segmentation," in 2022 30th International Conference on Electrical Engineering (ICEE), 2022: IEEE, pp. 587-591.
[12] F. Yu and V. Koltun, "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv:1511.07122, 2015.
[13] F. Yu, V. Koltun, and T. Funkhouser, "Dilated residual networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 472-480.
[14] M. Moreno Lopez and J. Ventura, "Dilated convolutions for brain tumor segmentation in MRI scans," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers 3, 2018: Springer, pp. 253-262.
[15] C. Chen, X. Liu, M. Ding, J. Zheng, and J. Li, "3D dilated multi-fiber network for real-time brain tumor segmentation in MRI," in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, 2019: Springer, pp. 184-192.
[16]T. Yang, Y. Zhou, L. Li, and C. Zhu, "DCU-Net: Multi-scale U-Net for brain tumor segmentation," Journal of X-ray Science and Technology, vol. 28, no. 4, pp. 709-726, 2020.
[17] N. Tustison and J. Gee, "N4ITK: Nick’s N3 ITK implementation for MRI bias field correction," Insight Journal, vol. 9, 2009.
[18] S. Wu, S. Zhong, and Y. Liu, "Deep residual learning for image steganalysis," Multimedia tools and applications, vol. 77, pp. 10437-10453, 2018.
[19] O. Oktay et al., "Attention u-net: Learning where to look for the pancreas," arXiv preprint arXiv:1804.03999, 2018.
[20] M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian, "A real-time algorithm for signal analysis with the help of the wavelet transform," in Wavelets: Time-Frequency Methods and Phase Space Proceedings of the International Conference, Marseille, France, December 14–18, 1987, 1990: Springer, pp. 286-297.
[21] C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso, "Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations," in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, 2017: Springer, pp. 240-248.
[22] U. Ruby and V. Yendapalli, "Binary cross entropy with deep learning technique for image classification," Int. J. Adv. Trends Comput. Sci. Eng, vol. 9, no. 10, 2020.
[23]J. Zhang, Z. Jiang, J. Dong, Y. Hou, and B. Liu, "Attention gate resU-Net for automatic MRI brain tumor segmentation," IEEE Access, vol. 8, pp. 58533-58545, 2020.
[24] S. Vijay, T. Guhan, K. Srinivasan, P. Vincent, and C.-Y. Chang, "MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net," Frontiers in public health, vol. 11, p. 1091850, 2023.
[25]Z. Jiang, C. Ding, M. Liu, and D. Tao, "Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5, 2020: Springer, pp. 231-241. | ||
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