Skip to main content

Pyramid-Based Fully Convolutional Networks for Cell Segmentation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11073)

Abstract

The low contrast and irregular cell shapes in microscopy images cause difficulties to obtain the accurate cell segmentation. We propose pyramid-based fully convolutional networks (FCN) to segment cells in a cascaded refinement manner. The higher-level FCNs generate coarse cell segmentation masks, attacking the challenge of low contrast between cell inner regions and the background. The lower-level FCNs generate segmentation masks focusing more on cell details, attacking the challenge of irregular cell shapes. The FCNs in the pyramid are trained in a cascaded way such that the residual error between the ground truth and upper-level segmentation is propagated to the lower-level and draws the attention of the lower-level FCNs to find the cell details missed from the upper-levels. The fine cell details from lower-level FCNs are gradually fused into the coarse segmentation from upper-level FCNs so as to obtain a final precise cell segmentation mask. On the ISBI cell segmentation challenge dataset and a newly collected dataset with high-quality ground truth, our method outperforms the state-of-the-art methods.

Keywords

  • Fully Convolutional Network (FCNs)
  • Irregular Cell Shape
  • Detailed Cell
  • Laplacian Pyramid
  • Gaussian Pyramid

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-00937-3_77
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-00937-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

References

  1. 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

    CrossRef  Google Scholar 

  2. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

    Google Scholar 

  3. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  4. He, K., et al.: Mask R-CNN. In: ICCV (2017)

    Google Scholar 

  5. Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. In: Readings in Computer Vision, pp. 671–679 (1987)

    Google Scholar 

  6. Denton, E.L., Chintala, S., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: NIPS (2015)

    Google Scholar 

  7. Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 519–534. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_32

    CrossRef  Google Scholar 

  8. WWW: Web page of the ISBI cell tracking challenge. http://www.celltrackingchallenge.net

  9. Maka, M., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11), 1609–1617 (2014)

    CrossRef  Google Scholar 

  10. Ciresan, D., et al.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NIPS (2012)

    Google Scholar 

  11. LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: NIPS (1990)

    Google Scholar 

  12. Meijering, E.: Cell segmentation: 50 years down the road [life sciences]. IEEE Sig. Process. Mag. 29(5), 140–145 (2012)

    CrossRef  Google Scholar 

Download references

Acknowledgement

This project was supported by NSF CAREER award IIS-1351049 and NSF EPSCoR grant IIA-1355406.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhaozheng Yin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Zhao, T., Yin, Z. (2018). Pyramid-Based Fully Convolutional Networks for Cell Segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11073. Springer, Cham. https://doi.org/10.1007/978-3-030-00937-3_77

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00937-3_77

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00936-6

  • Online ISBN: 978-3-030-00937-3

  • eBook Packages: Computer ScienceComputer Science (R0)