Abstract
This paper presents a new approach to improve brain tumor segmentation in Magnetic Resonance Imaging (MRI) using brain temperature distribution and U-Net convolutional neural network. As we presented in our recent works, brain tumors generate more heat than healthy tissues and based on tumor temperature profile we can determine tumor borders and reinforce segmentation in conventional MRI sequences such as T1-weighted, T1-weighed contrast-enhanced, T2-weighted, and Flair images. In the present work, U-Net architecture was used for brain tumor segmentation from thermal images. The results were compared to segmentation with U-Net in Flair images. Pennes bioheat transfer equation discretized with Finite Difference Method (FDM), was used to calculate the temperature distribution of the brain with tumors, 2% of additional Gaussian noise was added to the calculated temperature and transformed to grayscale thermal images. Then, U-Net was used for brain tumor segmentation from thermal images. We have generated 276 images to train the U-Net model, and 25 images were used to test the model. The dataset is containing thermal and the corresponding Flair images with the ground truth of tumors of the same patient at the same level. The training computation time of U-Net in 276 thermal images was about 15 h using NVIDIA GTX 1060 GPU with 6 GB memory. The obtained segmentation from thermal images was compared to segmentation of tumors from Flair images using U-Net in 10 images. An average of 0.33% of tumors was detected only in thermal images, and an average of 2.05% of healthy tissues was detected only in thermal images. The obtained results demonstrate the importance of thermal information of brain tumors to improve segmentation in MRI towards an effective diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bousselham, A., Bouattane, O., Youssfi, M., Raihani, A.: Toward an efficient brain tumor extraction using level set method and pennes bioheat equation. In: 4th IEEE International Colloquium on Information Science and Technology (CiSt), Tangier, Morocco, pp. 762–767 (2016). https://doi.org/10.1109/cist.2016.7804989
Bousselham, A., Bouattane, O., Youssfi, M., Raihani, A.: An efficient level set speed function based on temperature changes for brain tumor segmentation. In: Khoukhi, F., Bahaj, M., Ezziyyani, M. (eds.) AIT2S 2018. LNNS, vol. 66, pp. 121–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11914-0_13
Bousselham, A., Bouattane, O., Youssfi, M., Raihani, A.: Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area. Int. J. Biomed. Imaging, Article ID 1758948 (2019). https://doi.org/10.1155/2019/1758948
Pennes, H.H.: Analysis on tissue arterial blood temperature in the resting human forearm. Appl. Physiol. 1(2), 93–122 (1948). https://doi.org/10.1152/jappl.1948.1.2.93
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Kumar, S., Negi, A., Singh, J.N.: Semantic segmentation using deep learning for brain tumor MRI via fully convolution neural networks. In: Satapathy, S., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems, SIST, vol. 106, pp. 11–19. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1742-2_2
Kermi, A., Mahmoudi, I., Khadir, M.T.: Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018, LNCS, vol. 11384, pp. 37–48. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_4
Smistad, E., Falch, T.L., Bozorgi, M., Elster, A.C., Lindseth, F.: Medical image segmentation on gpus - a comprehensive review. Med. Image Anal. 20(1), 1–18 (2015). https://doi.org/10.1016/j.media.2014.10.012
Kalaiselvi, T., Sriramakrishnan, P., Somasundaram, K.: Survey of using GPU CUDA programming model in medical image analysis. Inf. Med. Unlocked 9, 133–144 (2017). https://doi.org/10.1016/j.imu.2017.08.001
Wissler, E.H.: Pennes’ 1948 paper revisited. J. Appl. Physiol. 85(1), 35–41 (1998). https://doi.org/10.1152/jappl.1998.85.1.35
Marcinkiewicz, M., Nalepa, J., Lorenzo, P.R., Dudzik, W., Mrukwa, G.: Segmenting brain tumors from MRI using cascaded multi-modal U-Nets. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) Brainlesion 2018, LNCS, vol. 11384, pp. 13–24. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_2
Luna, M., Park, S.H.: 3D patchwise U-Net with transition layers for MR brain segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) Brainlesion 2018, LNCS, vol. 11383, pp. 394–403, Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_40
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017). https://doi.org/10.1109/TPAMI.2016.2572683
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv Prepr. arXiv1412.6980 (2014)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013). https://doi.org/10.2196/jmir.2930
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bousselham, A., Bouattane, O., Youssfi, M., Raihani, A. (2020). Improved Brain Tumor Segmentation in MRI Images Based on Thermal Analysis Model Using U-Net and GPUs. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-030-36677-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-36677-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36676-6
Online ISBN: 978-3-030-36677-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)