Journal article
Automated Three-Dimensional Tracing of Neurons in Confocal and Brightfield Images
Microscopy and microanalysis, v 9(4), pp 296-310
Aug 2003
PMID: 12901764
Abstract
Automated three-dimensional (3-D) image analysis methods are
presented for tracing of dye-injected neurons imaged by fluorescence
confocal microscopy and HRP-stained neurons imaged by transmitted-light
brightfield microscopy. An improved algorithm for adaptive 3-D
skeletonization of noisy images enables the tracing. This algorithm
operates by performing connectivity testing over large N × N
× N voxel neighborhoods exploiting the sparseness of the
structures of interest, robust surface detection that improves upon
classical vacant neighbor schemes, improved handling of process ends or
tips based on shape collapse prevention, and thickness-adaptive
thinning. The confocal image stacks were skeletonized directly. The
brightfield stacks required 3-D deconvolution. The results of
skeletonization were analyzed to extract a graph representation.
Topological and metric analyses can be carried out using this
representation. A semiautomatic method was developed for reconnection
of dendritic fragments that are disconnected due to insufficient dye
penetration, an imaging deficiency, or skeletonization errors.
Metrics
Details
- Title
- Automated Three-Dimensional Tracing of Neurons in Confocal and Brightfield Images
- Creators
- Wenyun He - Rensselaer Polytechnic InstituteThomas A. Hamilton - Rensselaer Polytechnic InstituteAndrew R. Cohen - Rensselaer Polytechnic InstituteTimothy J. Holmes - Autoquant Imaging Inc., Watervliet, NY 12180Christopher Pace - University at Albany, State University of New YorkDonald H. Szarowski - New York State Department of HealthJames N. Turner - New York State Department of HealthBadrinath Roysam - Rensselaer Polytechnic Institute
- Publication Details
- Microscopy and microanalysis, v 9(4), pp 296-310
- Publisher
- Cambridge University Press
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000184437100007
- Scopus ID
- 2-s2.0-0042739762
- Other Identifier
- 991019296795104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Materials Science, Multidisciplinary
- Microscopy