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End-to-End Nested Multi-Attention Network for 3D Brain Tumor Segmentation

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Cognitive Computing – ICCC 2020 (ICCC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12408))

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Abstract

Utilizing the powerful feature learning ability of deep learning, researchers have proposed a variety of effective methods for brain tumor segmentation in three-dimensional medical images. However, the existing approaches often need to be processed in stages pipeline, without considering the anatomical nested structural characteristics of brain tumors, thus affecting the accuracy and efficiency of tumor segmentation. In this paper, we propose the Nested Multi-Attention mechanism Network (NMA-Net) tailored for brain tumors, which can effectively connect the sub-segmentation tasks of different organizations, and can directly conduct end-to-end training. By using the segmentation result of the tumor peripheral tissue as a kind of soft attention to the tumor segmentation task, it can make the subsequent network focus more on the region of interest, which makes it possible to obtain more accurate segmentation results. Besides, we transform multi-class segmentation tasks into multiple binary sub-segmentation tasks. Experiments on the BraTS’2017 Challenge Dataset show that the proposed NMA-Net framework is very suitable for organ tissue segmentation with nested anatomical structures. Here, our single-view model achieves the best segmentation performance compared with the exiting approaches, and the multi-view fusion model also achieves the state-of-the-art performance on the TC and ET sub-regions.

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References

  1. Chen, X., Liew, J.H., Xiong, W., Chui, C.-K., Ong, S.-H.: Focus, segment and erase: an efficient network for multi-label brain tumor segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 674–689. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_40

    Chapter  Google Scholar 

  2. 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

    Chapter  Google Scholar 

  3. Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 179–187. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_19

    Chapter  Google Scholar 

  4. Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770–778 (2016)

    Google Scholar 

  7. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR 2017, pp. 2261–2269 (2017)

    Google Scholar 

  8. Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287–297. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_25

    Chapter  Google Scholar 

  9. Jégou, S., Drozdzal, M., Vázquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: CVPR Workshops 2017, pp. 1175–1183 (2017)

    Google Scholar 

  10. Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38

    Chapter  Google Scholar 

  11. Kamnitsas, K., et al.: Deepmedic for brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, vol. 10154, pp. 138–149. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_14

    Chapter  Google Scholar 

  12. Litjens, G.J.S., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  13. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  14. 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

    Chapter  Google Scholar 

  15. Tseng, K., Lin, Y., Hsu, W.H., Huang, C.: Joint sequence learning and cross-modality convolution for 3D biomedical segmentation. In: CVPR 2017, pp. 3739–3746 (2017)

    Google Scholar 

  16. Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  17. Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155, 159–168 (2017)

    Article  Google Scholar 

  18. Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_16

    Chapter  Google Scholar 

  19. Yu, L., et al.: Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 287–295. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_33

    Chapter  Google Scholar 

  20. Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: Proceedings of AAAI 2017, pp. 66–72 (2017)

    Google Scholar 

  21. Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: 3D brain tumor segmentation through integrating multiple 2D FCNNs. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 191–203. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_17

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Xinrui Zhuang & Yujiu Yang

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  1. Xinrui Zhuang
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  2. Yujiu Yang
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Correspondence to Yujiu Yang .

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Editors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Shenzhen, China

    Yujiu Yang

  2. IBM Research – Thomas J. Watson Research, New York, NY, USA

    Lei Yu

  3. Kingdee International Software Group Co., Ltd, Shenzhen, China

    Liang-Jie Zhang

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Zhuang, X., Yang, Y. (2020). End-to-End Nested Multi-Attention Network for 3D Brain Tumor Segmentation. In: Yang, Y., Yu, L., Zhang, LJ. (eds) Cognitive Computing – ICCC 2020. ICCC 2020. Lecture Notes in Computer Science(), vol 12408. Springer, Cham. https://doi.org/10.1007/978-3-030-59585-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-59585-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59584-5

  • Online ISBN: 978-3-030-59585-2

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