Browse all publications by topic
Browse all publications by year
- S. Schmitt, J.-F. Evers,
C. Duch, M. Scholz, and K. Obermayer. New Methods for the
Computer-Assisted 3D Reconstruction of Neurons from Confocal Image Stacks.
.
NeuroImage, 23:1283-1298, 2004.
(FTP PDF, 3827 kb)
Exact geometrical reconstructions of neuronal architecture are
indispensable for the investigation of neuronal function. Neuronal shape is
important for the wiring of networks, and dendritic architecture strongly
affects neuronal integration and firing properties as demonstrated by
modeling approaches. Confocal microscopy allows to scan neurons with
submicron resolution. However, it is still a tedious task to reconstruct
complex dendritic trees with fine structures just above voxel resolution. We
present a framework assisting the reconstruction. User time investment is
strongly reduced by automatic methods which fit a skeleton and a surface to
the data, while the user can interact, and thus, keeps full control to ensure
a high quality reconstruction. The reconstruction process comprises a
successive gain of metric parameters. First a structural description of the
neuron is built, including the topology and the exact dendritic lengths and
diameters. We use generalized cylinders with circular cross-sections. The
user provides a rough initialization by marking the branching points. The
axes and radii are fitted to the data by minimizing an energy-functional
which is regularized by a smoothness constraint. The investigation of
proximity to other structures throughout dendritic trees requires a precise
surface reconstruction. In order to achieve accuracy of 0.1 micron and below,
we additionally implemented a segmentation algorithm based on geodesic active
contours which allows for arbitrary cross-sections and uses locally adapted
thresholds. In summary, this new reconstruction tool saves time and increases
quality as compared to other methods which have previously been applied to
real neurons.
|
