Development of Brain MRI Image Segmentation program using UNET++ network for radiosurgery planning

Bui Ha1, Tuấn Kiên Nguyễn, Dương Trần, Ngọc Toàn Trần, Quang Tuấn Hồ, Thu Trang Vũ
1 Hanoi University of Science and Technology

Main Article Content

Abstract

Image processing is one of the most important and widely used techniques in the medical field. Magnetic Resonance Imaging (MRI) can provide diagnostic images with high contrast and high resolution, especially for low-density tissue. Therefore, applications to support tumor prediction are researched and developed. In this paper, we use applied artificial intelligence to identify and detect tumors using the UNET ++ deep learning model, which achieved results with a recognition rate of about 80%. The results for a great deal of built-in functionality in the built-in physician support software system in practice.

Article Details

References

[1]. Jerrold T. Bushberg Ph.D., J. Anthony Seibert Ph.D., Edwin M. Leidholdt Jr. Ph.D., John M. Boone Ph.D., “The essential physics of medical imaging,” Lippincott Williams & Wilkins, pp. 8-9, 2002.
[2]. Rebecca, S., Diana, M., Eric, J., Choonsik, L., Heather, F., Michael, F., Robert, G., Randell, K., Mark, H., Douglas, M., et al. “Use of Diagnostic Imaging Studies and Associated Radiation Exposure for Patients Enrolled in Large Integrated Health Care Systems,” 1996–2010. JAMA 2012, 307, 2400 –2409.
[3]. Hsiao, C.-J., Hing, E., Ashman, J., “Trends in electronic health record system use among office-based physicians: United States”, 2007–2012. Natl. Health Stat. Rep., 1, pp. 1–18, 2014
[4]. Vincent S. Khoo, David P. Dearnaley, David J. Finnigan, Anwar Padhani, Steven F. Tanner, martin o. leach, “Magnetic resonance imaging (MRI): considerations and applications in radiotherapy treatment planning”, Radiother Oncol, vol. 42, pp. 1-15, 1997.
[5]. Y, B. J. Matuszewski, Lik-Kwan Shark, C.J. Moore, “A novel medical image segmentation method using dynamic programming”, IEEE, 2007, pp. 69-74.
[6]. H. Suzuki and J. Toriwaki, “Automatic segmentation of head MRI images by knowledge guided thresholding,” Computerized Med. Imag. Graphics, vol. 15, no. 4, pp. 233–240, July–Aug. 1991.
[7]. C. Li, D. B. Godlgof, and L. O. Hall, “Knowledge-based classification and tissue labeling of MR images of human brain,” IEEE Trans. Med. Imag., vol. 12, pp. 740–750, Dec. 1993.
[8]. J. W. Snell, M. B. Merickel, J. M. Ortega, J. C. Goble, J. R. Brookeman, and N. F. Kassell, “Segmentation of the brain from 3-D MRI using a hierarchical active surface template,” in Proc. SPIE Conf. Medical Imaging, 1994.
[9]. T. Kapur, W. E. L. Grimson, W. M. Wells III, and R. Kikinis, “Segmentation of brain tissue from magnetic resonance images,” Med. Imag. Anal., vol. 1, no. 2, 1996.
[10]. F. Pannizzo, M. J. B. Stallmeyer, J. Friedman, R. J. Jennis, J. Zabriskie, C. Pland, R. Zimmerman, J. P. Whalen, and P. T. Cahill, “Quantitative MRI studies for assessment of multiple sclerosis,” Magn. Reson. Med., vol. 24, pp. 90–99, 1992.
[11]. F. Gorunescu, “Data mining techniques in computer-aided diagnosis: Non-invasive cancer detection”, PWASET 25, 427–430, 2007.
[12]. S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz and D. Terzopoulos, "Image Segmentation Using Deep Learning: A Survey" in IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3059968.
[13]. Suzuki, K. Overview of deep learning in medical imaging. Radiol Phys Technol 10, 257–273 (2017). https://doi.org/10.1007/s12194-017-0406-5.
[14]. J. Long, E. Shelhamer and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” The IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, 2015.
[15]. Olaf Ronneberger, Philipp Fischer, Thomas Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, ArXiv: 1505.04597, 2015.
[16]. Z.W. Zhou, M.M.R. Siddiquee, N. Tajbakhsh, and J.M. Liang, “UNet++: A Nested U-Net Architecture for Medical Image Segmentation,” Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp: 3-11, 2018.
[17]. B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694.
[18]. S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117.
[19]. S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018).
[20]. Foivos Diakogiannis, Francois Waldner, Peter Caccetta and Chen Wu, “ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data”, ISPRS Journal Photogrammetry and Remote Sensing, Vol 162, pp 94-114, 2020.
[21]. Jianxin Zhang, Zongkang Jiang, Jing Dong and Bin Liu, “Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation”, EEE Access, vol. 8, pp. 58533-58545, 2020