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Neuroinformatics

, 7:213 | Cite as

MDL Constrained 3-D Grayscale Skeletonization Algorithm for Automated Extraction of Dendrites and Spines from Fluorescence Confocal Images

  • Xiaosong Yuan
  • Joshua T. Trachtenberg
  • Steve M. Potter
  • Badrinath Roysam
Article
First Online:
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Accepted:

Abstract

This paper presents a method for improved automatic delineation of dendrites and spines from three-dimensional (3-D) images of neurons acquired by confocal or multi-photon fluorescence microscopy. The core advance presented here is a direct grayscale skeletonization algorithm that is constrained by a structural complexity penalty using the minimum description length (MDL) principle, and additional neuroanatomy-specific constraints. The 3-D skeleton is extracted directly from the grayscale image data, avoiding errors introduced by image binarization. The MDL method achieves a practical tradeoff between the complexity of the skeleton and its coverage of the fluorescence signal. Additional advances include the use of 3-D spline smoothing of dendrites to improve spine detection, and graph-theoretic algorithms to explore and extract the dendritic structure from the grayscale skeleton using an intensity-weighted minimum spanning tree (IW-MST) algorithm. This algorithm was evaluated on 30 datasets organized in 8 groups from multiple laboratories. Spines were detected with false negative rates less than 10% on most datasets (the average is 7.1%), and the average false positive rate was 11.8%. The software is available in open source form.

Keywords

Dendritic spines Minimum description length Grayscale 3-D skeletonization 3-D morphology Graph theory 3-D microscopy 
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Notes

Acknowledgements

The image analysis aspects of this work were supported by NIH Biomedical Research Partnerships Grant R01 EB005157, by the Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821), and by Rensselaer Polytechnic Institute. The Potter laboratory images were collected by SMP and David Kantor in collaboration with Erin Schuman and Scott Fraser. Trachtenberg lab work was supported by NIMH grant P50 MH077972.

Supplementary material

12021_2009_9057_MOESM1_ESM.zip (1.3 mb)
ESM 1 (ZIP 1.25 mb)
12021_2009_9057_MOESM2_ESM.ppt (6.2 mb)
ESM 2 (PPT 6.15 mb)
12021_2009_9057_MOESM3_ESM.doc (128 kb)
ESM 3 Table of adjustable parameters used in the software, and their descriptions (DOC 127 kb)

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Copyright information

© Humana Press Inc. 2009

Authors and Affiliations

  • Xiaosong Yuan
    • 1
  • Joshua T. Trachtenberg
    • 2
  • Steve M. Potter
    • 3
  • Badrinath Roysam
    • 1
  1. 1.Jonsson Engineering Center, Center for Subsurface Sensing & Imaging SystemsRensselaer Polytechnic InstituteTroyUSA
  2. 2.Department of NeurobiologyDavid Geffen School of MedicineLos AngelesUSA
  3. 3.Laboratory for Neuroengineering, Coulter Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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