International Conference on Image Analysis and Processing

ICIAP 2015: Image Analysis and Processing — ICIAP 2015 pp 39-49 | Cite as

Implicit Boundary Learning for Connectomics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9279)

Abstract

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

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