3D Reconstruction of Neural Circuits from Serial EM Images

A basic requirement for reconstructing and understanding complete
circuit diagrams of neuronal processing units is the availability
of electron microscopic 3D data sets of large ensembles of neurons.
A recently developed technique, "Serial Block Face Scanning
Electron Microscopy" (SBFSEM, Denk and Horstmann 2004) allows
automatic sectioning and imaging of biological tissue inside the
vacuum chamber of a scanning electron microscope. Image stacks
generated with this technology have a resolution sucient to
distinguish different cellular compartments, including synaptic
structures. Such an image stack contains thousands of images and is
recorded with a voxel size of 23 nm in the x- and y-directions and
30 nm in the z-direction. Consequently a tissue block of 1 mm3
produces 63 terabytes of data. Therefore new concepts for managing
large data sets and automated image processing are required. I
developed an image segmentation and 3D reconstruction software,
which allows precise contour tracing of cell membranes and
simultaneously displays the resulting 3D structure. The software
contains two stand-alone packages: Neuron2D and Neuron3D, both
oering an easy-to-operate graphical user interface (GUI). The
software package Neuron2D provides the following image processing
functions: • Image Registration: Combination of multiple SBFSEM
image tiles. • Image Preprocessing: Filtering of image stacks.
Implemented are Gaussian and Non-Linear-Diusion lters in 2D and 3D.
This step enhances the contrast between contour lines and image
background, leading to a higher signal-to-noise ratio, thus further
improving detection of membrane borders. • Image Segmentation: The
implemented algorithms extract contour lines from the preceding
image and automatically trace the contour lines in the following
images (z-direction), taking into account the previous image
segmentation. They also permit image segmentation starting at any
position in the image stack. In addition, manual interaction is
possible. To visualize 3D structures of neuronal circuits the
additional software Neuron3D was developed. The program relies on
the contour line information provided by Neuron2D to implement a
surface reconstruction algorithm based on dynamic time warping.
Additional rendering techniques, such as shading and texture
mapping, are provided. The detailed anatomical reconstruction
provides a framework for computational models of neuronal circuits.
For example in ies, where moving retinal images lead to appropriate
course control signals, the circuit reconstruction of
motion-sensitive neurons can help to further understand the neural
processing of visual motion in ies.