Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation

  • Mustafa Gökhan Uzunbaş
  • Chao Chen
  • Dimitris Metaxsas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


We present a new algorithm for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. Our method selects a collection of nodes from the watershed merging tree as the proposed segmentation. This is achieved by building a conditional random field (CRF) whose underlying graph is the merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our algorithm outperforms state-of-the-art methods. Both the inference and the training are very efficient as the graph is tree-structured. Furthermore, we develop an interactive segmentation framework which selects uncertain regions for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.


Conditional Random Field Watershed EM Segmentation User Interaction 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mustafa Gökhan Uzunbaş
    • 1
  • Chao Chen
    • 1
  • Dimitris Metaxsas
    • 1
  1. 1.CBIMRutgers UniversityPiscatawayUSA

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