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Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images

Aaron Scherzinger, Florian Kleene, Cathrin Dierkes, Friedemann Kiefer, Klaus H. Hinrichs, Xiaoyi Jiang
Pattern Recognition - 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings, Lecture Notes in Computer Science, Volume 9796, page 105-116 - 2016
Detection, segmentation, and quantification of individual cell nuclei is a standard task in biomedical applications. Due to the increasing volume of acquired image data, it is not possible to rely on manual labeling and object counting. Instead, automated image processing methods have to be applied. Especially in three-dimensional data, one of the major challenges is the separation of touching cell nuclei in densely packed clusters. In this paper, we propose a method for automated detection and segmentation of immunostained cell nuclei in ultramicroscopy images. Our algorithm utilizes interactive learning and voxel classification to obtain a foreground segmentation and subsequently performs the splitting process for each cluster using a multi-step watershed approach. We have evaluated our results using reference images manually labeled by domain experts and compare our approach to state-of-the art methods.

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BibTex references

@inproceedings{SKDKHJ16,
  author = {Scherzinger, Aaron and Kleene, Florian and Dierkes, Cathrin and Kiefer, Friedemann and Hinrichs, Klaus H. and Jiang, Xiaoyi},
  title = {Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images},
  booktitle = {Pattern Recognition - 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings},
  series = {Lecture Notes in Computer Science},
  volume = {9796},
  pages = {105--116},
  year = {2016},
  editor = {Bodo Rosenhahn, Bjoern Andres},
  publisher = {Springer},
  url = {http://viscg.uni-muenster.de/publications/2016/SKDKHJ16},
}

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