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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)

Abstract

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.

Keywords

Image segmentation Filaments segmentation Neural networks Microscopy images 

Notes

Acknowledgements

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).

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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

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