Densely Connected Stacked U-network for Filament Segmentation in Microscopy Images

  • Yi LiuEmail author
  • Wayne Treible
  • Abhishek Kolagunda
  • Alex Nedo
  • Philip Saponaro
  • Jeffrey Caplan
  • Chandra Kambhamettu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


Segmenting filamentous structures in confocal microscopy images is important for analyzing and quantifying related biological processes. However, thin structures, especially in noisy imagery, are difficult to accurately segment. In this paper, we introduce a novel deep network architecture for filament segmentation in confocal microscopy images that improves upon the state-of-the-art U-net and SOAX methods. We also propose a strategy for data annotation, and create datasets for microtubule and actin filaments. Our experiments show that our proposed network outperforms state-of-the-art approaches and that our segmentation results are not only better in terms of accuracy, but also more suitable for biological analysis and understanding by reducing the number of falsely disconnected filaments in segmentation.


Image segmentation Filaments segmentation Neural networks Microscopy images 



The National Institute of Health R01 grant GM097587 supported this work. Microscopy access was supported by grants from the NIH (P20 GM103446 and S10 OD016361).


  1. 1.
    Almi’ani, M.M., Barkana, B.D.: A modified region growing based algorithm to vessel segmentation in magnetic resonance angiography (2015)Google Scholar
  2. 2.
    Chang, S., Kulikowski, C.A., Dunn, S.M., Levy, S.: Biomedical image skeletonization: a novel method applied to fibrin network structures (2001)Google Scholar
  3. 3.
    Costa, P., et al.: Towards adversarial retinal image synthesis. arXiv preprint arXiv:1701.08974 (2017)
  4. 4.
    Fan, Z., Wu, Y., Lu, J., Li, W.: Automatic pavement crack detection based on structured prediction with the convolutional neural network. arXiv preprint arXiv:1802.02208 (2018)
  5. 5.
    Fu, H., Xu, Y., Wong, D.W.K., Liu, J.: Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 698–701. IEEE (2016)Google Scholar
  6. 6.
    Fuller, N., Aboudarham, J., Bentley, R.: Filament recognition and image cleaning on meudon h\(\alpha \) spectroheliograms (2005)Google Scholar
  7. 7.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)Google Scholar
  8. 8.
    Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). Scholar
  9. 9.
    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). Scholar
  10. 10.
    Saponaro, P., et al.: DeepXScope: segmenting microscopy images with a deep neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 91–98 (2017)Google Scholar
  11. 11.
    Smith, M.B., Li, H., Shen, T., Huang, X., Yusuf, E., Vavylonis, D.: Segmentation and tracking of cytoskeletal filaments using open active contours (2010)Google Scholar
  12. 12.
    Xiao, X., Geyer, V.F., Bowne-Anderson, H., Howard, J., Sbalzarini, I.F.: Automatic optimal filament segmentation with sub-pixel accuracy using generalized linear models and B-spline level-sets. Med. Image Anal. 32, 157–172 (2016)CrossRefGoogle Scholar
  13. 13.
    Xu, T., Vavylonis, D., Huang, X.: 3D actin network centerline extraction with multiple active contours (2014)CrossRefGoogle Scholar
  14. 14.
    Xu, T., et al.: SOAX: a software for quantification of 3D biopolymer networks. Sci. Rep. 5, 9081 (2015)CrossRefGoogle Scholar
  15. 15.
    Yue, G., Jiang, L., Liu, C., Yang, G., Ai, J., Chen, X.: Automated segmentation of microtubules in Cryo-EM images with excessive white noise. In: Kim, K.J., Joukov, N. (eds.) Information Science and Applications (ICISA) 2016. LNEE, vol. 376, pp. 339–348. Springer, Singapore (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yi Liu
    • 1
    Email author
  • Wayne Treible
    • 1
  • Abhishek Kolagunda
    • 1
  • Alex Nedo
    • 1
  • Philip Saponaro
    • 1
  • Jeffrey Caplan
    • 1
  • Chandra Kambhamettu
    • 1
  1. 1.University of DelawareNewarkUSA

Personalised recommendations