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Pyramid-Based Fully Convolutional Networks for Cell Segmentation

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


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.


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

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  • DOI: 10.1007/978-3-030-00937-3_77
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  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).

    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).

    CrossRef  Google Scholar 

  8. WWW: Web page of the ISBI cell tracking challenge.

  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 

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

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Correspondence to Zhaozheng Yin .

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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.

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