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Automatic Fusion of Segmentation and Tracking Labels

  • Cem Emre AkbaşEmail author
  • Vladimír Ulman
  • Martin Maška
  • Florian Jug
  • Michal Kozubek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)

Abstract

Labeled training images of high quality are required for developing well-working analysis pipelines. This is, of course, also true for biological image data, where such labels are usually hard to get. We distinguish human labels (gold corpora) and labels generated by computer algorithms (silver corpora). A naturally arising problem is to merge multiple corpora into larger bodies of labeled training datasets. While fusion of labels in static images is already an established field, dealing with labels in time-lapse image data remains to be explored. Obtaining a gold corpus for segmentation is usually very time-consuming and hence expensive. For this reason, gold corpora for object tracking often use object detection markers instead of dense segmentations. If dense segmentations of tracked objects are desired later on, an automatic merge of the detection-based gold corpus with (silver) corpora of the individual time points for segmentation will be necessary. Here we present such an automatic merging system and demonstrate its utility on corpora from the Cell Tracking Challenge. We additionally release all label fusion algorithms as freely available and open plugins for Fiji (https://github.com/xulman/CTC-FijiPlugins).

Keywords

Label fusion Image annotation Segmentation labels Tracking labels 

Notes

Acknowledgements

This work has been supported by the German Federal Ministry of Research and Education (BMBF) under the code 031L0102 (de.NBI), and by the Czech Science Foundation (GACR), grant P302/12/G157.

References

  1. 1.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903–921 (2004)CrossRefGoogle Scholar
  2. 2.
    Langerak, T.R., van der Heide, U.A., Kotte, A.N.T.J., Viergever, M.A., van Vulpen, M., Pluim, J.P.W.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med. Imaging 29(12), 2000–2008 (2010)CrossRefGoogle Scholar
  3. 3.
    Lampert, T.A., Stumpf, A., Gançarski, P.: An empirical study into annotator agreement, ground truth estimation, and algorithm evaluation. IEEE Trans. Image Process. 25(6), 2557–2572 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(6), 436–444 (2015)CrossRefGoogle Scholar
  5. 5.
    Coutu, D.L., Schroeder, T.: Probing cellular processes by long-term live imaging-historic problems and current solutions. J. Cell Sci. 126(17), 3805–3815 (2013)CrossRefGoogle Scholar
  6. 6.
    Amat, F., et al.: Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nat. Methods 11(9), 951–958 (2014)CrossRefGoogle Scholar
  7. 7.
    Maška, M., Ulman, V., Svoboda, D., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11), 1609–1617 (2014)CrossRefGoogle Scholar
  8. 8.
    Ulman, V., Maška, M., Magnusson, K.E.G., et al.: An objective comparison of cell-tracking algorithms. Nat. Methods 14(12), 1141–1152 (2017)CrossRefGoogle Scholar
  9. 9.
    Matula, P., Maška, M., Sorokin, D.V., Matula, P., Ortiz-de Solórzano, C., Kozubek, M.: Cell tracking accuracy measurement based on comparison of acyclic oriented graphs. PLOS ONE 10(12), 1–19 (2015)CrossRefGoogle Scholar
  10. 10.
    Schlesinger, D., Jug, F., Myers, G., Rother, C., Kainmueller, D.: Crowd sourcing image segmentation with iaSTAPLE. In: IEEE International Symposium on Biomedical Imaging, pp. 401–405 (2017)Google Scholar
  11. 11.
    Liu, X., Montillo, A., Tan, E.T., Schenck, J.F.: iSTAPLE: improved label fusion for segmentation by combining STAPLE with image intensity. In: SPIE Medical Imaging, pp. 866–920 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cem Emre Akbaş
    • 1
    Email author
  • Vladimír Ulman
    • 1
    • 2
  • Martin Maška
    • 1
  • Florian Jug
    • 2
  • Michal Kozubek
    • 1
  1. 1.Center for Biomedical Image Analysis, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic
  2. 2.Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany

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