Automated three-dimensional tracing of neurons in confocal and brightfield images.
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 x N x 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.
He
Wenyun
W
Electrical Computer and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA.
Hamilton
Thomas A
TA
Cohen
Andrew R
AR
Holmes
Timothy J
TJ
Pace
Christopher
C
Szarowski
Donald H
DH
Turner
James N
JN
Roysam
Badrinath
B
D000818:Animals; D002415:Cats; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods; D009474:Neurons / Q000166:cytology / Q000648:ultrastructure*