Reconstructing neuronal morphology from microscopy stacks using fast marching

Sreetama Basu; Daniel Racoceanu

doi:10.1109/ICIP.2014.7025730

Reconstructing neuronal morphology from microscopy stacks using fast marching

Sreetama Basu, Daniel Racoceanu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

Automated algorithms to build accurate models of 3D neuronal arborization is much in demand due to large volume of microscopy data. We present a tracking algorithm for automatic and reliable extraction of neuronal morphology. It is robust to ambiguous branch discontinuities, variability of intensity and curvature of fibres, arbitrary branch cross-sections, noise and irregular background illumination. We complete the presentation of our method with demonstration of its performance on synthetic data modeling challenging scenarios and on confocal microscopy data of Olfactory Projection fibres from DIADEM data set with promising results.

Original language	English
Title of host publication	2014 IEEE International Conference on Image Processing, ICIP 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3597-3601
Number of pages	5
ISBN (Electronic)	9781479957514
DOIs	https://doi.org/10.1109/ICIP.2014.7025730
State	Published - 28 Jan 2014
Externally published	Yes

Publication series

Name	2014 IEEE International Conference on Image Processing, ICIP 2014

Keywords

Fast Forward Marching
Gradient Vector Flow
Neuronal morphology

Access to Document

10.1109/ICIP.2014.7025730

Cite this

Basu, S., & Racoceanu, D. (2014). Reconstructing neuronal morphology from microscopy stacks using fast marching. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 3597-3601). Article 7025730 (2014 IEEE International Conference on Image Processing, ICIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7025730

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abstract = "Automated algorithms to build accurate models of 3D neuronal arborization is much in demand due to large volume of microscopy data. We present a tracking algorithm for automatic and reliable extraction of neuronal morphology. It is robust to ambiguous branch discontinuities, variability of intensity and curvature of fibres, arbitrary branch cross-sections, noise and irregular background illumination. We complete the presentation of our method with demonstration of its performance on synthetic data modeling challenging scenarios and on confocal microscopy data of Olfactory Projection fibres from DIADEM data set with promising results.",

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doi = "10.1109/ICIP.2014.7025730",

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Reconstructing neuronal morphology from microscopy stacks using fast marching. / Basu, Sreetama; Racoceanu, Daniel.
2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3597-3601 7025730 (2014 IEEE International Conference on Image Processing, ICIP 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Automated algorithms to build accurate models of 3D neuronal arborization is much in demand due to large volume of microscopy data. We present a tracking algorithm for automatic and reliable extraction of neuronal morphology. It is robust to ambiguous branch discontinuities, variability of intensity and curvature of fibres, arbitrary branch cross-sections, noise and irregular background illumination. We complete the presentation of our method with demonstration of its performance on synthetic data modeling challenging scenarios and on confocal microscopy data of Olfactory Projection fibres from DIADEM data set with promising results.

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Reconstructing neuronal morphology from microscopy stacks using fast marching

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Keywords

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