Active Graph Matching for Automatic Joint Segmentation and Annotation of C. elegans

  • Dagmar Kainmueller
  • Florian Jug
  • Carsten Rother
  • Gene Myers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time.


Manual Segmentation Graph Match Active Shape Model Nucleus Location Active Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dagmar Kainmueller
    • 1
  • Florian Jug
    • 1
  • Carsten Rother
    • 2
  • Gene Myers
    • 1
  1. 1.Max Planck Institute of Molecular Cell Biology and GeneticsGermany
  2. 2.Computer Vision Lab DresdenTechnical University DresdenGermany

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