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Automating the process of neural circuit reconstruction on a large-scale is one of the foremost challenges in the field of neuroscience. In this study we examine the methodology for circuit reconstruction from three-dimensional light microscopy (LM) stacks of images. We show how the minimal error-rate of an ideal reconstruction procedure depends on the density of labeled neurites, giving rise to the fundamental limitation of an LM based approach for neural circuit research. Circuit reconstruction procedures typically involve steps related to neuron labeling and imaging, and subsequent image pre-processing and tracing of neurites. In this study, we focus on the last step--detection of traces of neurites from already pre-processed stacks of images. Our automated tracing algorithm, implemented as part of the Neural Circuit Tracer software package, consists of the following main steps. First, image stack is filtered to enhance labeled neurites. Second, centerline of the neurites is detected and optimized. Finally, individual branches of the optimal trace are merged into trees based on a cost minimization approach. The cost function accounts for branch orientations, distances between their end-points, curvature of the merged structure, and its intensity. The algorithm is capable of connecting branches which appear broken due to imperfect labeling and can resolve situations where branches appear to be fused due the limited resolution of light microscopy. The Neural Circuit Tracer software is designed to automatically incorporate ImageJ plug-ins and functions written in MatLab and provides roughly a 10-fold increases in speed in comparison to manual tracing.

Automated Tracing of Neurites from Light Microscopy Stacks of Images