Implicit Boundary Learning for Connectomics

  • Tobias Maier
  • Thomas Vetter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9279)


Segmentation of complete neurons in 3D electron microscopy images is an important task in Connectomics. A common approach for automatic segmentation is to detect membrane between neurons in a first step. This is often done with a random forest. We propose a new implicit boundary learning scheme that optimizes the segmentation error of neurons instead of the classification error of membrane. Given a segmentation, optimal labels for boundary between neurons and for non-boundary are found automatically and are used for training. In contrast to training random forests with labels for membrane and intracellular space, this novel training method does not require many labels for the difficult to label membrane and reduces the segmentation error significantly.


Random Forest Minimum Span Tree Intracellular Space Segmentation Error Dense Label 
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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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of BaselBaselSwitzerland

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