Abstract
In this paper, we propose a 3D convolutional neural network targeting at the segmentation of brain tumor. There are different types of brain tumors and our focus is one common type named glioma. The proposed network is efficient and balances the tradeoff between the number of parameters and accuracy of segmentation. It consists of Anisotropic Block, Dilated Parallel Residual Block, and Feature Refinement Module. The Anisotropic Block applies anisotropic convolutional kernels on different branches. In addition, the Dilated Parallel Residual Block incorporates 3D depthwise and separable convolutions to reduce the amount of required parameters dramatically, while multiscale dilated convolutions enlarge the receptive field. The Feature Refinement Module prevents global contextual information loss. Our method is evaluated on the BRATS 2017 dataset. The results show that our method achieved competitive performance among all compared methods, with a reduced number of parameters. The ablation study also proves that each individual block or module is effective.
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References
Bakas, S., et al.: Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Jia, H., et al.: 3D APA-Net: 3D adversarial pyramid anisotropic convolutional network for prostate segmentation in mr images. IEEE Trans. Med. Imag. (2019)
Li, W., Wang, G., Fidon, L., Ourselin, S., Cardoso, M.J., Vercauteren, T.: On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 348–360. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_28
Liu, D., et al.: Densely connected large kernel convolutional network for semantic membrane segmentation in microscopy images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2461–2465. IEEE (2018)
Liu, D., Zhang, D., Song, Y., Zhang, F., O’Donnell, L.J., Cai, W.: 3D large kernel anisotropic network for brain tumor segmentation. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 444–454. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04239-4_40
Ma, J., Yang, X.: Automatic brain tumor segmentation by exploring the multi-modality complementary information and cascaded 3D lightweight CNNs. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 25–36. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_3
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imag. 34(10), 1993–2024 (2015)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision, pp. 565–571 (2016)
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imag. 35(5), 1240–1251 (2016)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: CVPR (2018)
Subbanna, N.K., Precup, D., Collins, D.L., Arbel, T.: Hierarchical probabilistic gabor and MRF segmentation of brain tumours in MRI volumes. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 751–758. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_94
Wang, H., et al.: Segmenting neuronal structure in 3D optical microscope images via knowledge distillation with teacher-student network. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 228–231. IEEE (2019)
Wen, P.Y., et al.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28(11), 1963–1972 (2010)
Wu, Y., et al.: Vessel-Net: retinal vessel segmentation under multi-path supervision. In: Shen, D., et al. (eds.) MICCAI 2019. Lecture Notes in Computer Science, vol. 11764, pp. 264–272. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_30
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 334–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_20
Zhang, C., et al.: MS-GAN: GAN-based semantic segmentation of multiple sclerosis lesions in brain magnetic resonance imaging. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2018)
Zhang, D., et al.: Panoptic segmentation with an end-to-end cell R-CNN for pathology image analysis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 237–244. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_27
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 6230–6239 (2017)
Zhao, X., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)
Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 637–645. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_73
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Zhang, D. et al. (2019). Efficient 3D Depthwise and Separable Convolutions with Dilation for Brain Tumor Segmentation. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_45
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