A semi-automatic tool for the connectivity tracking of neuronal processes acquired using electron microscopy

Abstract

We describe an image processing tool for helping to organize, annotate and map neuronal circuitry. The tools use high-resolution images serially acquired via scanning electron microscopy (SEM). First, various image processing filters are applied to the serial images to automatically sort and stitch them. In a second step, images are aligned, equalized and filtered to generate 3D volumetric image stacks. Finally, within the 3D volume, the neural processes selected by the user are automatically segmented via parallel programming. Thus, the tool renders a model in which pre- and postsynaptic elements become identified in a connectivity matrix.

We describe an image processing tool for helping to organize, annotate and map neuronal circuitry. The tools use high-resolution images serially acquired via scanning electron microscopy (SEM). First, various image processing filters are applied to the serial images to automatically sort and stitch them. In a second step, images are aligned, equalized and filtered to generate 3D volumetric image stacks. Finally, within the 3D volume, the neural processes selected by the user are automatically segmented via parallel programming. Thus, the tool renders a model in which pre- and postsynaptic elements become identified in a connectivity matrix.