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Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

Lookup NU author(s): Stefan Wernitznig, Dr Claire Rind, Dr Gerd Leitinger

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Abstract

Background: Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern.New method: The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation.Results: For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result.Comparison with existing methods: Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity.Conclusion: Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons. (C) 2016 Elsevier B.V. All rights reserved.


Publication metadata

Author(s): Wernitznig S, Sele M, Urschler M, Zankel A, Polt P, Rind FC, Leitinger G

Publication type: Article

Publication status: Published

Journal: Journal of Neuroscience Methods

Year: 2016

Volume: 264

Pages: 16-24

Print publication date: 01/05/2016

Online publication date: 27/02/2016

Acceptance date: 22/02/2016

ISSN (print): 0165-0270

ISSN (electronic): 1872-678X

Publisher: Elsevier

URL: http://dx.doi.org/10.1016/j.jneumeth.2016.02.019

DOI: 10.1016/j.jneumeth.2016.02.019


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