Abstract
The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.
Keywords
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Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 68(1), 7–30 (2018). https://doi.org/10.3322/caac.21442
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017). https://doi.org/10.1016/j.media.2016.05.004
Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A.M., Hamed, H.F.A.: Classification of brain tumor MRIs using a kernel support vector machine. In: Li, H., Nykänen, P., Suomi, R., Wickramasinghe, N., Widén, G., Zhan, M. (eds.) WIS 2016. CCIS, vol. 636, pp. 151–160. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44672-1_13
Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A.M., Hamed, H.F.A.: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J. Image Video Process. 2018(1), 97 (2018). https://doi.org/10.1186/s13640-018-0332-4
Soltaninejad, M., et al.: Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. Comput. Assist. Radiol. Surg. 12(2), 183–203 (2017). https://doi.org/10.1007/s11548-016-1483-3
Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A.M., Hamed, H.F.A.: Design and implementation of a computer-aided diagnosis system for brain tumor classification. In: 2016 28th International Conference on Microelectronics (ICM), pp. 73–76, 17–20 December 2016. https://doi.org/10.1109/ICM.2016.7847911
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016). https://doi.org/10.1109/TMI.2016.2538465
Pereira, S., Oliveira, A., Alves, V., Silva, C.A.: On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: a preliminary study. In: 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG), pp. 1–4, 16–18 February 2017. https://doi.org/10.1109/ENBENG.2017.7889452
de Brebisson, A., Montana, G.: Deep neural networks for anatomical brain segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20–28, June 2015. https://doi.org/10.1109/CVPRW.2015.7301312
Xiao, Z., et al.: A deep learning-based segmentation method for brain tumor in MR images. In: 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), pp. 1–6 (2016). https://doi.org/10.1109/ICCABS.2016.7802771
Casamitjana, A., Puch, S., Aduriz, A., Vilaplana, V.: 3D convolutional neural networks for brain tumor segmentation: a comparison of multi-resolution architectures. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. Lecture Notes in Computer Science, vol. 10154, pp. 150–161. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_15
Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., Zhang, Y.: Brain tumor segmentation using a fully convolutional neural network with conditional random fields. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. Lecture Notes in Computer Science, vol. 10154, pp. 75–87. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_8
Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 61–72. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_6
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034 (2015). https://doi.org/10.1109/ICCV.2015.123
Miller, J.W., Goodman, R., Smyth, P.: On loss functions which minimize to conditional expected values and posterior probabilities. IEEE Trans. Inf. Theor. 39(4), 1404–1408 (1993). https://doi.org/10.1109/18.243457
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, ICML 2015, vol. 37, pp. 448–456, 06–11 July 2015. http://dl.acm.org/citation.cfm?id=3045118.3045167, JMLR.org
Le, H.T., Pham, H.T.T.: Brain tumour segmentation using U-Net based fully convolutional networks and extremely randomized trees. Vietnam J. Sci. Technol. Eng. 60(3), 19–25 (2018). https://doi.org/10.31276/VJSTE.60(3).191
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Abd-Ellah, M.K., Khalaf, A.A.M., Awad, A.I., Hamed, H.F.A. (2019). TPUAR-Net: Two Parallel U-Net with Asymmetric Residual-Based Deep Convolutional Neural Network for Brain Tumor Segmentation. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_9
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