Automatic reconstruction of dendrite morphology from optical section stacks

Authors:
S. Urban;S. M. O'Malley;B. Walsh;A. Santamaría-Pang;P. Saggau;C. Colbert;I. A. Kakadiaris
Affiliations:
Dept. of Comp. Sci., Computational Biomedicine Lab, Houston, TX;Dept. of Comp. Sci., Computational Biomedicine Lab, Houston, TX;Dept. of Comp. Sci., Computational Biomedicine Lab, Houston, TX;Dept. of Comp. Sci., Computational Biomedicine Lab, Houston, TX;Div. of Neuroscience, Baylor College of Medicine, Houston, TX;Dept. of Biology & Biochemistry, U. of Houston, Houston, TX;Dept. of Comp. Sci., Computational Biomedicine Lab, Houston, TX
Venue:
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Year:
2006

Citing 6
Cited 2

Representations of quasi-Newton matrices and their use in limited memory methods

Mathematical Programming: Series A and B
A limited memory algorithm for bound constrained optimization

SIAM Journal on Scientific Computing
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization

ACM Transactions on Mathematical Software (TOMS)
Penalized-Distance Volumetric Skeleton Algorithm

IEEE Transactions on Visualization and Computer Graphics
Rapid automated three-dimensional tracing of neurons from confocal image stacks

IEEE Transactions on Information Technology in Biomedicine
Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform

IEEE Transactions on Image Processing

Automatic centerline extraction of irregular tubular structures using probability volumes from multiphoton imaging

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

The function of the human brain arises from computations that occur within and among billions of nerve cells known as neurons. A neuron is composed primarily of a cell body (soma) from which emanates a collection of branching structures (dendrites). How neuronal signals are processed is dependent on the dendrites' specific morphology and distribution of voltage-gated ion channels. To understand this processing, it is necessary to acquire an accurate structural analysis of the cell. Toward this end, we present an automated reconstruction system which extracts the morphology of neurons imaged from confocal and multi-photon microscopes. As we place emphasis on this being a rapid (and therefore automated) process, we have developed several techniques that provide high-quality reconstructions with minimal human interaction. In addition to generating a tree of connected cylinders representing the reconstructed neuron, a computational model is also created for purposes of performing functional simulations. We present visual and statistical results from reconstructions performed both on real image volumes and on noised synthetic data from the Duke-Southampton archive.