Skip to main content
SpringerLink
Log in

Brain tumor segmentation via C-dense convolutional neural network

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

A lightweight convolutional neural network model is presented for automatic brain tumor segmentation from multimodal MRI images. To explore 3D spatial contexts with computational efficiency, we introduce a Complementary Convolution Unit (CCU), which reduces memory overhead but not sacrifices accuracy by replacing 3D convolution with two complementary 2D convolutions. Specifically, 2D convolutions in different orientations are used to capture different in-slice features. Furthermore, we enhance the CCUs with dense connections to speed up network training and facilitate feature reuse. To mitigate interference between brain tissues, the task of multi-label brain tumor segmentation is decomposed into three binary segmentation subtasks. For each subtask, we fuse the segmentations on multi-axes to further improve the segmentation accuracy. Validations and comparisons with recent methods conducted on the BRATS17 validation dataset have demonstrated the effectiveness of proposed model. The experiment results showed that we achieved average Dice scores of 0.890, 0.808 and 0.753 for the whole tumor, tumor core and enhancing tumor core, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://www.med.upenn.edu/sbia/brats2017/data.html.

  2. https://ipp.cbica.upenn.edu/.

  3. https://www.tensorflow.org/.

  4. https://niftynet.io/.

References

  1. Chen, X., Hao Liew, J., Xiong, W.: Focus, Segment and erase: an efficient network for multi-label brain tumor segmentation. In: Proceedings of the 15th European Conference on Computer Vision, pp. 654–669. Springer, Munich, Germany (2018)

  2. Li, Y., Shen, L.: Deep learning based multimodal brain tumor diagnosis. In: 3th International MICCAI Brainlesion Workshop, pp. 149–158. Springer, Quebec, Canada (2017)

  3. Wang, G., Li, W., Ourselin, S., et al.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: 3th International MICCAI Brainlesion Workshop, pp. 178–190. Springer, Quebec, Canada (2017)

  4. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the 28th IEEE conference on computer vision and pattern recognition, pp. 3431–3440. IEEE, Boston, Massachusetts (2015)

  5. Chen, L., Bentley, P., Mori, K., et al.: DRINet for medical image segmentation. IEEE Trans. Med. Imag. 37(11), 2453–2462 (2018)

    Article  Google Scholar 

  6. Havaei, M., Davy, A., Warde-Farley, D., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  7. Kamnitsas, K., Ledig, C., Newcombe, V.F., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  8. Chen, F., Ding, Y., Wu, Z., et al.: An improved framework called Du++ applied to brain tumor segmentation. In: 15th International Computer Conference on Wavelet Active Media Technology and Information Processing, pp. 85–88. IEEE, Chengdu, China (2018)

  9. Pereira, S., Alves, V., Silva, C.A.: Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI. In: 21th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 706–714. Springer, Granada, Canada (2018)

  10. Huang, G., Liu, Z., Weinberger, K.Q., et al.: Densely connected convolutional networks. In: Proceedings of the 30th IEEE conference on computer vision and pattern recognition, pp. 4700–4708. IEEE, Honolulu, Hawaii (2017)

  11. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: 18th International Conference on Medical image computing and computer-assisted intervention, pp. 234–241. Springer, Munich, Germany (2015)

  12. Litjens, G., Kooi, T., Bejnordi, B.E., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  13. Marcinkiewicz, M., Nalepa, J., Lorenzo, P.R., et al.: Segmenting brain tumors from MRI using cascaded multi-modal U-Nets. In: 4th International MICCAI Brainlesion Workshop, pp. 13–24. Springer, Granada, Canada (2018)

  14. Meng, Z., Fan, Z., Zhao, Z., et al.: ENS-Unet: End-to-End noise suppression U-Net for brain tumor segmentation. In: 40th International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5886–5889. IEEE, Honolulu, Hawaii (2018)

  15. Zhao, X., Wu, Y., Song, G., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)

    Article  Google Scholar 

  16. Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 4th International Conference on 3D Vision, pp. 565–571. IEEE, Stanford, CA, (2016)

  17. Isensee, F., Kickingereder, P., Wick, W., et al.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: 4th International MICCAI Brainlesion Workshop, pp. 287–297. Springer, Granada, Canada (2018)

  18. Casamitjana, A., Catà, M., Sánchez, I., et al.: Cascaded V-Net using ROI masks for brain tumor segmentation. In: 3th International MICCAI Brainlesion Workshop, pp. 381–391. Springer, Quebec, Canada (2017)

  19. Zhou, C., Ding, C., Lu, Z., et al.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: 21th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 637–645. Springer, Granada, Spain (2018)

  20. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the 30th IEEE conference on computer vision and pattern recognition, pp. 1251–1258. IEEE, Honolulu, Hawaii (2017)

  21. He, K., Zhang, X., Ren, S., et al.: Identity mappings in deep residual networks. In: Proceedings of the 14th European conference on computer vision, pp. 630–645. Springer, Amsterdam, Netherlands (2016)

  22. He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the 15th IEEE international conference on computer vision, pp. 1026–1034. IEEE, Santiago, Chile (2015)

  23. Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imag. 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  24. Bakas, S., Akbari, H., Sotiras, A., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data. 4, 170117 (2017)

    Article  Google Scholar 

  25. Abadi, M., Barham, p., Chen, J., et al.: TensorFlow: A system for large-scale machine learning. In: 12th Symposium on Operating Systems Design and Implementation, pp. 265–283. USENIS, Savannah, GA (2016)

  26. Gibson, E., Li, W., Sudre, C., et al.: NiftyNet: a deep-learning platform for medical imaging. Computer Methods Programs Biomed. 158, 113–122 (2018)

    Article  Google Scholar 

  27. Li, W., Wang, G., Fidon, L., et al.: On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. In: 25th International Conference on Information Processing in Medical Imaging, pp. 348–360. Springer, Appalachian State University, North Carolina (2017)

  28. Kingma, D. P., Ba, J.: Adam: A method for stochastic optimization. (2014)

  29. Kamnitsas, K., Bai, W., Ferrante, E., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: 21th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 450–462. Springer, Granada, Spain (2018)

  30. Islam, M., Ren, H.: Class balanced pixelnet for neurological image segmentation. In: Proceedings of the 6th International Conference on Bioinformatics and Computational Biology, pp. 83–87, ACM, Chengdu, China (2018)

  31. Kim, G.: Brain tumor segmentation using deep fully convolutional neural networks. In: 3th International MICCAI Brainlesion Workshop, pp. 344–357. Springer, Quebec, Canada (2017)

  32. Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the 29th IEEE conference on computer vision and pattern recognition, pp. 2818–2826. IEEE, Las Vegas, Nevada (2016)

  33. Grivalsky, S., Tamajka, M., Benesova, W.: Segmentation of gliomas in magnetic resonance images using recurrent neural networks. In: 42th International Conference on Telecommunications and Signal Processing, pp. 539–542. IEEE, Budapest, Hungary (2019)

  34. Das, J., Patel, R., Pankajakshan, V.: Brain Tumor Segmentation Using Discriminator Loss. In: 25th National Conference on Communications, pp. 1–6. IEEE, Bangalore, Indian (2019)

  35. Zhao, X., Wu, Y., Song, G., et al.: 3D Brain Tumor Segmentation Through Integrating Multiple 2D FCNNs. In: 3th International MICCAI Brainlesion Workshop, pp. 191–203. Springer, Quebec, Canada (2017)

  36. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., et al.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: 19th International Conference on Medical image computing and computer-assisted intervention, pp. 424–432. Springer, Athens, Greece (2016)

Download references

Acknowledgements

This work was partially supported by NSFC (11771160) and STPF (2019H0016).

Author information

Authors and Affiliations

  1. School of School of Economics and Finance, Huaqiao University, Xiamen, 361021, China

    Ye Wang

  2. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Jialin Peng & Zhongdao Jia

Authors
  1. Ye Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jialin Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhongdao Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jialin Peng or Zhongdao Jia.

Ethics declarations

Conflict of interest

There is not conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Peng, J. & Jia, Z. Brain tumor segmentation via C-dense convolutional neural network. Prog Artif Intell 10, 147–156 (2021). https://doi.org/10.1007/s13748-021-00232-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-021-00232-8

Keywords

Search

Navigation