Abstract
Analysis of medical image is a useful method that can support doctors in medical diagnosis. The development of deep learning models is essential and widely applied in image processing and computer vision. Application of machine learning and artificial intelligent in brain tumor diagnosis brings an accuracy and efficiency in medical treatment field. In this research paper, we present a method for determining and segmenting the brain tumor region in the medical image dataset based on 3D Generative Adversarial Network (3D-GAN) model. We first explore the state-of-the-art methods and recent approaches in such field. Our proposed 3D-GAN model consist of three steps: (i) pre-processing data, (ii) building an architecture of multi-scaled GAN model, and (iii) modifying loss function. The last our contribution is creating an application to visualize 3D models that representation of medical resonance brain images with the incorporation of the chosen models to determine exactly the region containing brain tumors. Comparing to the existing methods, our proposed model obtained better performance and accuracy.
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References
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27 (2014)
Maji, D., Sigedar, P., Singh, M.: Attention Res-UNet with guided decoder for semantic segmentation of brain tumors. Biomed. Signal Process. Control 71, 103077 (2022). https://doi.org/10.1016/j.bspc.2021.103077
Zhaoa, Z., Wang, Y., Liu, K., Yang, H., Sun, Q., Qiao, H.: Semantic segmentation by improved generative adversarial networks. In: Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and Information Sciences (2021). https://doi.org/10.48550/ARXIV.2104.09917
Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. In: Computer Vision and Pattern Recognition (2021). https://doi.org/10.48550/ARXIV.2107.02314
Van Nguyen, S., Tran, H.M., Maleszka, M.: Geometric modeling: background for processing the 3d objects. Appl. Intell. 51(8), 6182–6201 (2021). https://doi.org/10.1007/s10489-020-02022-6
Nguyen, V.S., Tran, M.H., Vu, H.M.Q.: An improved method for building a 3D model from 2D DICOM. In: Proceedings of International Conference on Advanced Computing and Applications (ACOMP), pp. 125–131. IEEE (2018). ISBN 978-1-5386-9186-1
Sinh, N.V., Ha, T.M., Truong, L.S.: Application of geometric modeling in visualizing the medical image dataset. J. SN Comput. Sci. 1(5), 254 (2020)
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
Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. J. Neural Netw. 121, 74–87 (2020). https://doi.org/10.1016/j.neunet.2019.08.025
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. J. IEEE Trans. Med. Imaging (2019). https://doi.org/10.48550/arXiv.1912.05074
Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: The Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Cicek, O., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net learning dense volumetric segmentation from sparse annotation. In: Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and Information Sciences (2016). https://arxiv.org/abs/1606.06650
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-Net. IEEE Geosci. Remote Sens. Lett. 15(5), (2018). https://doi.org/10.1109/lgrs.2018.2802944. ISSN 1558-0571
Li, R., et al.: DeepUNet: a deep fully convolutional network for pixel-level sea-land segmentation. arXiv (2017). https://doi.org/10.48550/ARXIV.1709.00201
Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv (2018). https://doi.org/10.48550/ARXIV.1804.03999
Chen, H., Qin, Z., Ding, Y., Lan, T.: Brain tumor segmentation with generative adversarial nets. In: 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 301–305 (2019). https://doi.org/10.1109/ICAIBD.2019.8836968
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 231–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_22
Isensee, F., Jäger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnU-Net for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 118–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72087-2_11
Futrega, M., Milesi, A., Marcinkiewicz, M., Ribalta, P.: Optimized U-Net for brain tumor segmentation. arXiv preprint arXiv:2110.03352 (2021)
Luu, H.M., Park, S.-H.: Extending nn-UNet for brain tumor segmentation. In: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (2021). https://doi.org/10.48550/arXiv.2112.04653
Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.: SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. J. Neuroinform. 16(3–4), 383–392 (2018)
Cirillo, M.D., Abramian, D., Eklund, A.: Vox2Vox: 3D-GAN for brain tumour segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 274–284. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72084-1_25
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and Information Sciences (2015). https://doi.org/10.48550/ARXIV.1512.03385
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and Information Sciences (2016). https://doi.org/10.48550/ARXIV.1608.04117
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Machine Learning (cs.LG), FOS: Computer and Information Sciences (2014). https://doi.org/10.48550/ARXIV.1412.6980
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This research is funded by International University, VNU-HCM under grant number SV2021-IT-02.
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Khanh Phung, L., Van Nguyen, S., Duy Le, T., Maleszka, M. (2022). A Research for Segmentation of Brain Tumors Based on GAN Model. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_30
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