Skip to main content
SpringerLink
Log in

BEM-RCNN Segmentation Based on the Inadequately Labeled Moving Mesenchymal Stem Cells

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11663))

Included in the following conference series:

Abstract

This paper addresses the challenging task of moving mesenchymal stem cell segmentation in digital time-lapse microscopy sequences. A convolutional neural network (CNN) based pipeline is developed to segment cells automatically. To accommodate the data in its unique nature, an efficient binarization enhancement policy is proposed to increase the tracing performance. Furthermore, to work with datasets with inadequate and inaccurate ground truth, a compensation algorithm is developed to enrich the annotation automatically, and thus ensure the training quality of the model. Experiments show that our model surpassed the state-of-the-art. Result of our model measured by SEG score is 0.818.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cell tracking challenge. http://celltrackingchallenge.net/

  2. Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.: Cell tracking via proposal generation and selection. arXiv preprint arXiv:1705.03386 (2017)

  3. Amat, F., et al.: Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nat. Methods 11(9), 951 (2014)

    Article  Google Scholar 

  4. Caplan, A.I.: Mesenchymal stem cells. J. Orthop. Res. 9(5), 641–650 (1991)

    Article  Google Scholar 

  5. Girshick, R.B.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)

    Google Scholar 

  6. Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 580–587 (2014)

    Google Scholar 

  7. Hawkins, D.M.: The problem of overfitting. J. Chem. Inform. Comput. Sci. 44(1), 1–12 (2004)

    Article  MathSciNet  Google Scholar 

  8. He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)

    Google Scholar 

  9. Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944 (2017)

    Google Scholar 

  10. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  12. Neumann, B., et al.: Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464(7289), 721 (2010)

    Article  Google Scholar 

  13. Rashid, R., Akram, M.U., Hassan, T.: Fully convolutional neural network for lungs segmentation from chest X-rays. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 71–80. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_9

    Chapter  Google Scholar 

  14. Real, R., Vargas, J.M.: The probabilistic basis of Jaccard’s index of similarity. Syst. Biol. 45(3), 380–385 (1996)

    Article  Google Scholar 

  15. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, NIPS 2015, vol. 1, pp. 91–99 (2015)

    Google Scholar 

  16. 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 

  17. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  18. Trepat, X., Chen, Z., Jacobson, K.: Cell migration. Compr. Physiol. 2(4), 2369 (2012)

    Google Scholar 

  19. Uccelli, A., Moretta, L., Pistoia, V.: Mesenchymal stem cells in health and disease. Nat. Rev. Immunol. 8(9), 726 (2008)

    Article  Google Scholar 

  20. Xie, X., Li, Y., Shen, L.: Active learning for breast cancer identification. arXiv preprint arXiv:1804.06670 (2018)

  21. Zimmer, C., et al.: On the digital trail of mobile cells. IEEE Signal Process. Mag. 23(3), 54–62 (2006)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors thank the IEEE International Symposium on Biomedical Imaging 2019 (ISBI19) cell tracking challenge [1] for providing the datasets aiding the development of this work.

Author information

Authors and Affiliations

  1. Queen Mary University of London, London, E1 4NS, UK

    Jingxiong Li & Qianni Zhang

  2. Hangzhou Dianzi University, Hangzhou, 310018, China

    Yaqi Wang

Authors
  1. Jingxiong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qianni Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qianni Zhang .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. University of Waterloo, Waterloo, ON, Canada

    Alfred Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, J., Wang, Y., Zhang, Q. (2019). BEM-RCNN Segmentation Based on the Inadequately Labeled Moving Mesenchymal Stem Cells. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27272-2_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27271-5

  • Online ISBN: 978-3-030-27272-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation