Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods

Şükrü Ozan

doi:10.33187/jmsm.1106012

EN

Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods

Abstract

Image segmentation has been a well-addressed problem in pattern recognition for the last few decades. As a sub-problem of image segmentation, the background separation in biomedical images generated by magnetic resonance imaging (MRI) has also been of interest in the applied mathematics literature. Level set evolution of active contours idea can successfully be applied to MRI images to extract the region of interest (ROI) as a crucial preprocessing step for medical image analysis. In this study, we use the classical level set solution to create binary masks of various brain MRI images in which black color implies background and white color implies the ROI. We further used the MRI image and mask image pairs to train a deep neural network (DNN) architecture called U-Net, which has been proven to be a successful model for biomedical image segmentation. Our experiments have shown that a properly trained U-Net can achieve a matching performance of the level set method. Hence we were able to train a U-Net by using automatically generated input and label data successfully. The trained network can detect ROI in MRI images faster than the level-set method and can be used as a preprocessing tool for more enhanced medical image analysis studies.

Keywords

Active contours, Deep neural networks, Evolving boundaries, Image segmentation, Level-set, MRI, Region of interest, U-net

References

[1] A. Krizhevsky, I. Sutskever, G. Hinton Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25 (2012), 1097-1105.
[2] M. Raissi, P. Perdikaris, G. E. Karniadakis Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys., 378 (2019), 686-707.
[3] M. Raissi, P. Perdikaris, G. E. Karniadakis Physics informed deep learning (Part I): Data-driven solutions of nonlinear partial differential equations, arXiv preprint arXiv:1711.10561 (2017).
[4] M. Raissi, P. Perdikaris, G. E. Karniadakis Physics informed deep learning (Part II): Data-driven discovery of nonlinear partial differential equations, arXiv preprint arXiv:1711.10566 (2017).
[5] S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, S. Kanchan Brain Tumor Classification (MRI), Kaggle (2020).
[6] J. A. Sethian Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, Cambridge University Press (1999).
[7] S. Osher, R. Fedkiw Level set methods and dynamic implicit surfaces, Appl. Math. Sci., 153 (2003).
[8] O. Ronneberger, P. Fischer, T. Brox U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, 9351 (2015), 234-241.
[9] Ş. Ozan Level set based contour evolution on brain MRI images, GitHub, https://github:com/sukruozan/level-set (2021).
[10] Ş. Ozan Unet training for MRI segmentation, GitHub, https://github:com/sukruozan/unet-mri (2021).

[11] C. Li, C. Xu, C. Gui, M.D. Fox Level set evolution without re-initialization: A new variational formulation, IEEE Trans. Image Process., 14 (2005), 2098-2104.
[12] S. Osher Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 79 (1988), 12-49.
[13] T. Tieleman, G. Hinton Lecture 6.5 - rmsprop, coursera, Coursera (2012).
[14] J. Katarzyna, W. M. Czarnecki On Loss Functions for Deep Neural Networks in Classification, arXiv preprint arXiv:1702.05659 (2017).
[15] S. Ioffe, C. Szegedy Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning, 37 (2015), 448-456.
[16] S. Ruder An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747 (2016).
[17] D. Pilutti, M. Buchert, S. Hadjidemetriou Registration of abdominal tumor DCE-MRI data based on deconvolution of joint statistics, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2611-2614.
[18] X. Xu, T. Meng, Q. Wen, M. Tao, P. Wang, K. Zong, Y. Shen Dynamic changes in vascular size and density in transgenic mice with Alzheimer’s disease, Impact Journals LLC, 12 (2020), 17224-17234.
[19] S. Urvashi, S. Meenakshi, P. Emjee Region of interest based selective coding technique for volumetric MR image sequence, Multimedia Tools and Applications, 80 (2021), 1-23.

Details

Primary Language

English

Subjects

Mathematical Sciences

Journal Section

Research Article

Authors

Şükrü Ozan ^*
0000-0002-3227-348X
Türkiye

Publication Date

April 30, 2023

Submission Date

April 19, 2022

Acceptance Date

September 16, 2022

Published in Issue

Year 2023 Volume: 6 Number: 1

DOI

https://doi.org/10.33187/jmsm.1106012

IZ

https://izlik.org/JA28KK87XS

APA

Ozan, Ş. (2023). Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods. Journal of Mathematical Sciences and Modelling, 6(1), 17-23. https://doi.org/10.33187/jmsm.1106012

AMA

1.Ozan Ş. Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods. Journal of Mathematical Sciences and Modelling. 2023;6(1):17-23. doi:10.33187/jmsm.1106012

Chicago

Ozan, Şükrü. 2023. “Training Data Generation for U-Net Based MRI Image Segmentation Using Level-Set Methods”. Journal of Mathematical Sciences and Modelling 6 (1): 17-23. https://doi.org/10.33187/jmsm.1106012.

EndNote

Ozan Ş (April 1, 2023) Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods. Journal of Mathematical Sciences and Modelling 6 1 17–23.

IEEE

[1]Ş. Ozan, “Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods”, Journal of Mathematical Sciences and Modelling, vol. 6, no. 1, pp. 17–23, Apr. 2023, doi: 10.33187/jmsm.1106012.

ISNAD

Ozan, Şükrü. “Training Data Generation for U-Net Based MRI Image Segmentation Using Level-Set Methods”. Journal of Mathematical Sciences and Modelling 6/1 (April 1, 2023): 17-23. https://doi.org/10.33187/jmsm.1106012.

JAMA

1.Ozan Ş. Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods. Journal of Mathematical Sciences and Modelling. 2023;6:17–23.

MLA

Ozan, Şükrü. “Training Data Generation for U-Net Based MRI Image Segmentation Using Level-Set Methods”. Journal of Mathematical Sciences and Modelling, vol. 6, no. 1, Apr. 2023, pp. 17-23, doi:10.33187/jmsm.1106012.

Vancouver

1.Şükrü Ozan. Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods. Journal of Mathematical Sciences and Modelling. 2023 Apr. 1;6(1):17-23. doi:10.33187/jmsm.1106012

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