(48-3) 15 * << * >> * Russian * English * Content * All Issues

Brain tumor segmentation by deep learning transfer methods using MRI images
E.Y. Shchetinin 1

1 Department of Mathematics, Financial University under the Government the Russian Federation,
125993, Moscow, Russia, Leningradsky Prospekt 49

 PDF, 1338 kB

DOI: 10.18287/2412-6179-CO-1366

Pages: 439-444.

Full text of article: English language.

Abstract:
Brain tumor segmentation is one of the most challenging tasks of medical image analysis. The diagnosis of patients with gliomas is based on the analysis of magnetic resonance images and manual segmentation of tumor boundaries. However, due to its time-consuming nature, there is a need for a fast and reliable automatic segmentation algorithm. In recent years, deep learning methods applied to brain tumor segmentation have shown promising results. In this paper, a deep neural network model based on U-Net neural network architecture is proposed for brain glioma segmentation. It is proposed to use deep convolutional neural network models pre-trained on the ImageNet dataset as U-Net encoders. Among such models, VGG16, VGG19, Mobilenetv2, Inception, Efficientnetb7, InceptionResnetV2, DenseNet201, DenseNet121 were used.
     The computational experimental analysis performed in the paper on a set of MRI brain images showed that the best encoder model for the above deep models was the DenseNet121 model with the following values of segmentation metrics Mean IoU of 91.34%, Mean Dice of 94.26%, Accuracy of 94.22%. The paper also comparatively analyses the results of the proposed segmentation method with several works of other authors. The comparative analysis of the segmentation results of the studied MRI images showed that the DenseNet121 model either surpassed or was comparable to the models proposed in the refereed papers in terms of segmentation accuracy metrics.

Keywords:
brain tumor, glioma, segmentation, U-Net model, encoder, pre-trained deep models.

Citation:
Shchetinin EY. Brain tumor segmentation by deep learning transfer methods using MRI images. Computer Optics 2024; 48(3): 439-444. DOI: 10.18287/2412-6179-CO-1366.

References:
  1. Mellinghoff IK, Gilbertson RJ. Brain tumors: challenges and opportunities to cure. J Clinical Oncology 2017; 35(1): 2343-2345.
  2. Despotović I, Goossens B, Philips W. MRI Segmentation of the human Brain: Challenges, methods, and applications. Comput Math Methods Med 2015; 2015: 450341. DOI: 10.1155/2015/450341.
  3. Marusina MYa. Modern types of tomography [in Russian]. Saint-Petersburg: Saint-Petersburg State University ITMO Publisher; 2006.
  4. Iqbal S, et al. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett 2018; 8(1): 5-28.
  5. Yamashita R, et al. Convolutional neural networks: an overview and application in radiology. Insights into Imaging 2018; 9(4): 611-629.
  6. Işın A, Direkoğlu C, Şah M. Review of MRI-based Brain tumor image segmentation using deep learning methods. Procedia Comput Sci 2016; 102: 317-324. DOI: 10.1016/j.procs.2016.09.407.
  7. Hoseini F, Shahbahrami A, Bayat P. An efficient implementation of deep convolutional neural networks for MRI segmentation. J Digit Imaging 2018; 31(5): 738-747.
  8. Long J, Shelhamer E, Darrel T. Fully convolutional networks for semantic segmentation. arXiv Preprint. 2015. Source: <https://arxiv.org/pdf/1411.4038.pdf>.
  9. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. arXiv Preprint. 2015. Source: <https://arxiv.org/pdf/1505.04597.pdf>.
  10. Lather M, Singh P. Investigating Brain tumor segmentation and detection techniques. Proc Comput Sci 2020; 167: 121-130. DOI: 10.1016/ j.procs.2020.03.189.
  11. Cai L, Gao J, Zhao D. A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 2020; 8(11): 713.
  12. Hussain S, Anwar SM, Majid M. Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 2018; 282: 248. DOI: 10.1016/j.neucom.2017.12.032.
  13. Stawiaski J. A pretrained DenseNet encoder for brain tumor segmentation. arXiv Preprint. 2018. Source: <https://arxiv.org/pdf/1811.07542.pdf>.
  14. Pravitasari A, Iriawan N, Almuhayar M, Azmi T, Fithriasari K, Purnami S. UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA Telecommunication, Computing, Electronics and Control 2020; 18(3): 1310-1318. DOI: 10.12928/TELKOMNIKA.v18i3.14753.
  15. Nasim A, Munem A, Islam, et al. Brain tumor segmentation using enhanced U-Net model with empirical analysis. arXiv Preprint. 2022. Source: <https://arxiv.org/abs/2210.13336>.
  16. Zheng P, Zhu X, Guo W. Brain tumour segmentation based on an improved U-Net. BMC Medical Imaging 2022; 22: 199. DOI: 10.1186/s12880-022-00931-1.
  17. Gupta A, Dixit M, Mishra VK, Singh A, Dayal A. Brain tumor segmentation from MRI images using deep learning techniques. arXiv Preprint. 2023. Source:           <https://arxiv.org/abs/2305.00257>.
  18. Deng W, Shi Q, Luo K, Yang Y, Ning N. Brain Tumor segmentation based on improved convolutional neural network in combination with nonquantifiable local texture feature. J Med Syst 2019; 43: 152. DOI: 10.1007/s10916-019-1289-2.
  19. Sharif MI, Li JP, Amin J, Sharif A. An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network. Complex Intell Syst 2021; 7: 2023-2036. DOI: 10.1007/s40747-021-00310-3.
  20. Magadza T, Viriri S. Deep learning for brain tumor segmentation: A survey of state-of-the-art. J Imaging 2021; 7(19): 254-263. DOI: 10.3390/jimaging7020019.
  21. Biratu ES, Schwenker F, Ayano YM, Debelee TG. A survey of brain tumor segmentation and classification algorithms. J Imaging 2021; 7: 179. DOI: 10.3390/jimaging7090179.
  22. Liu Z, Tong L, Chen L, Jiang Z, Zhou F. etc. Deep learning based brain tumor segmentation: A survey. Complex Intell Syst 2023; 9: 1001-1026. DOI: 10.1007/s40747-022-00815-5.
  23. Shchetinin EY. Detection of COVID-19 coronavirus infection in chest X-ray images with deep learning methods. Computer Optics 2022; 46(6): 963-970. DOI: 10.18287/2412-6179-CO-1077.
  24. Buda M, Saha A, Mazurowski A. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput Biol Med 2019; 109: 218-225. DOI: 10.1016/j.compbiomed.2019.05.002.
  25. Chollet F. Keras: Deep learning library for Theano and Tensorflow. 2023. Source: <https://www.datasciencecentral.com/keras-deep-learning-library-for-theano-and-tensorflow/>.
  26. Géron A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. 2nd ed. O’Reilly Media; 2019. ISBN: 1492032646.
  27. Keras: semantic segmentation metrics. 2023. Source: <https://keras.io/api/metrics/segmentation_metrics/>.
  28. Shchetinin EY, Glushkova AG. Arrhythmia detection using resampling and deep learning methods on unbalanced data. Computer Optics 2022; 46(6): 980-987. DOI: 10.18287/2412-6179-CO-1112.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20