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J Neurosci Methods. 2015 May 15;246:13-21. doi: 10.1016/j.jneumeth.2015.03.005. Epub 2015 Mar 10.

Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images.

Author information

1
Scientific Computing and Imaging Institute, University of Utah, United States; Department of Electrical and Computer Engineering, University of Utah, United States.
2
Scientific Computing and Imaging Institute, University of Utah, United States; School of Computing, University of Utah, United States.
3
National Center for Microscopy and Imaging Research, University of California, San Diego, United States.
4
Scientific Computing and Imaging Institute, University of Utah, United States; Department of Electrical and Computer Engineering, University of Utah, United States; School of Computing, University of Utah, United States. Electronic address: tolga@sci.utah.edu.

Abstract

BACKGROUND:

In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming.

NEW METHOD:

We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links.

RESULTS:

We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results.

COMPARISON WITH EXISTING METHODS:

Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method.

CONCLUSION:

Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication.

KEYWORDS:

3D segmentation; Connectomics; Electron microscopy; Image segmentation; Neuron reconstruction; Semi-automatic segmentation

PMID:
25769273
PMCID:
PMC4398646
DOI:
10.1016/j.jneumeth.2015.03.005
[Indexed for MEDLINE]
Free PMC Article

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