Advertisement

CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

  • Hongying Liu
  • Xiongjie Shen
  • Fanhua ShangEmail author
  • Feihang Ge
  • Fei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)

Abstract

This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct between-net connections to link features at the same resolution and transmit the detailed information from shallow layers to the deeper layers. Then we present a loss weighted sampling (LWS) scheme to eliminate the issue of imbalanced data. Experimental results on the BraTS 2017 dataset show that our framework outperforms the state-of-the-art segmentation algorithms, especially in terms of segmentation sensitivity.

Keywords

Brain tumor segmentation Cascaded U-Net Feature fusion Loss weighted sampling 

Notes

Acknowledgments

This work was supported by the State Key Program of National Natural Science of China (No. 61836009), the Project supported the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61621005), the Major Research Plan of the National Natural Science Foundation of China (Nos. 91438201 and 91438103), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048), the National Natural Science Foundation of China (Nos. 61976164, 61876220, 61876221, U1701267, U1730109, 61473215, 61871310, 61472306, and 61502369), the Program for Cheung Kong Scholars and Innovative Research Team in University (No. IRT_15R53), the Science Foundation of Xidian University (Nos. 10251180018 and 10251180019), the Fundamental Research Funds for the Central Universities under Grant (No. 20101195989), the National Science Basic Research Plan in Shaanxi Province of China (No. 2019JQ-657), and the Key Special Project of China High Resolution Earth Observation System-Young Scholar Innovation Fund.

References

  1. 1.
    Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Annual Conference on Medical Image Understanding and Analysis, pp. 506–517 (2017)Google Scholar
  2. 2.
    Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  3. 3.
    Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, P.M.: A convolutional neural network approach to brain tumor segmentation. In: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 195–208 (2015)Google Scholar
  4. 4.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  5. 5.
    Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-based segmentation of medical imaging data. arXiv preprint (2017). arXiv:1701.03056
  6. 6.
    Liu, H., Shang, F., Yang, S., Gong, M., Zhu, T., Jiao, L.: Sparse manifold regularized neural networks for polarimetric sar terrain classification. IEEE Trans. Neural Netw. Learn. Syst. (2019)Google Scholar
  7. 7.
    Lopez, M.M., Ventura, J.: Dilated convolutions for brain tumor segmentation in MRI scans. In: International MICCAI Brainlesion Workshop, pp. 253–262 (2017)Google Scholar
  8. 8.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)Google Scholar
  9. 9.
    Shen, H., Wang, R., Zhang, J., McKenna, S.: Multi-task fully convolutional network for brain tumour segmentation. In: Annual Conference on Medical Image Understanding and Analysis, pp. 239–248 (2017)Google Scholar
  10. 10.
    Shen, H., Wang, R., Zhang, J., McKenna, S.J.: Boundary-aware fully convolutional network for brain tumor segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 433–441. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_49 CrossRefGoogle Scholar
  11. 11.
    Wang, D., et al.: signADAM: learning confidences for deep neural networks (2019). arXiv: 1907.09008
  12. 12.
    Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: International MICCAI Brainlesion Workshop, pp. 178–190 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hongying Liu
    • 1
  • Xiongjie Shen
    • 1
  • Fanhua Shang
    • 1
    Email author
  • Feihang Ge
    • 2
  • Fei Wang
    • 3
  1. 1.Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial IntelligenceXidian UniversityXi’anChina
  2. 2.School of Information Science and TechnologyAichi Prefectural UniversityNagakuteJapan
  3. 3.Weill Cornell Medical SchoolCornell UniversityNew YorkUSA

Personalised recommendations