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

Stefan Wernitznig, Mariella Sele, Martin Urschler, Armin Zankel, Peter Pölt, F Claire Rind, Gerd Leitinger

Research output: Contribution to journalArticleResearchpeer-review

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.

LanguageEnglish
Pages16-24
Number of pages9
JournalJournal of neuroscience methods
Volume264
DOIs
StatusPublished - 27 Feb 2016

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Electron Scanning Microscopy
Neurons
Anatomy
Locusta migratoria
Grasshoppers
Software
Electrons
Costs and Cost Analysis

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  • Information, Communication & Computing
  • Human- & Biotechnology

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  • BioTechMed-Graz

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Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy. / Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F Claire; Leitinger, Gerd.

In: Journal of neuroscience methods, Vol. 264, 27.02.2016, p. 16-24.

Research output: Contribution to journalArticleResearchpeer-review

Wernitznig, Stefan ; Sele, Mariella ; Urschler, Martin ; Zankel, Armin ; Pölt, Peter ; Rind, F Claire ; Leitinger, Gerd. / Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy. In: Journal of neuroscience methods. 2016 ; Vol. 264. pp. 16-24.
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N2 - 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.

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