Nonlinear anisotropic diffusion filtering of three-dimensional image data from two-photon microscopy

Abstract

Two-photon microscopy in combination with novel fluorescent labeling techniques enables imaging of three-dimensional neuronal morphologies in intact brain tissue. In principle it is now possible to automatically reconstruct the dendritic branching patterns of neurons from 3-D fluorescence image stacks. In practice however, the signal-to-noise ratio can be low, in particular in the case of thin dendrites or axons imaged relatively deep in the tissue. Here we present a nonlinear anisotropic diffusion filter that enhances the signal-to-noise ratio while preserving the original dimensions of the structural elements. The key idea is to use structural information in the raw data-the local moments of inertia-to locally control the strength and direction of diffusion filtering. A cylindrical dendrite, for example, is effectively smoothed only parallel to its longitudinal axis, not perpendicular to it. This is demonstrated for artificial data as well as for in vivo two-photon microscopic data from pyramidal neurons of rat neocortex. In both cases noise is averaged out along the dendrites, leading to bridging of apparent gaps, while dendritic diameters are not affected. The filter is a valuable general tool for smoothing cellular processes and is well suited for preparing data for subsequent image segmentation and neuron reconstruction.

Spike timing-dependent selective strengthening of single climbing fibre inputs to Purkinje cells during cerebellar development

“…Data were analysed using custom-written macro in IgorPro (WaveMetrics). Image stacks were obtained after recordings, and processed offline using a non-linear anisotropic diffusion filter59. Two-dimensional (2D) projections were performed using ImageJ ( http://rsb.info.nih.gov/ij/).…”

1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

“…To make use of the advantages of highly detailed three-dimensional models and to cope with the complexity that comes with modeling large networks of neurons, we developed a method to couple state of the art general purpose neuron simulators, e.g., NEURON, with three-dimensional models of single neurons. This 1D/3D hybrid method includes automated tools to either reconstruct three-dimensional neuron morphologies from raw microscopy data (Broser et al, 2004; Queisser et al, 2008; Jungblut et al, 2011), from anatomically recorded data (Wolf et al, 2013) or from graph-structure morphologies as used, e.g., in the NEURON Simulator in the form of hoc-files or swc-files.…”

Optical manipulation for single‐cell studies

“…By measuring the position of the trap and the surface independently, the noise from the surface drift can be reduced to approximately one Angströ m [142]. Lang and coworkers [70] used this geometry to study the force-induced strand separation of a dye-labelled, 15-base-pair region of a double-stranded DNA. They found that the rupture force strongly depended on the direction of the applied force, which indicates the existence of distinct unbinding pathways for the two force loading modes.…”

“…Much research is directed towards the development of specific fluorophores such as the green fluorescent protein (GFP) or quantum dots [60] and thanks to the progress within the field, fluorescence microscopy is currently experiencing a boom. Equally important is the development of sensitive and fast detectors to record signals from single molecules even at the picosecond level [64][65][66] as well as new filtering techniques in order to precisely sort out minute energy differences [67][68][69][70]. Widely used fluorescent techniques include wide-field fluorescence microscopy, confocal laser scanning microscopy and multiphoton microscopy.…”