Abstract
Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons (MNs) and understand underlying principles of synaptic connectivity in a motor circuit. Our computational approach aims at the reconstruction of the neuron morphology, namely the robust segmentation of the neuron volumes from their surroundings with the simultaneous partitioning into their compartments, namely the soma, axon, and dendrites. We use the idea of cosegmentation, where every image along the z -axis (depth) is segmented using information from "neighboring" depths. We use 3-D Haar-like features to model appearance. Because soma and axon are determined by their distinctive shapes, we define an implicit shape representation of the 2-D segmentation sets to drive cosegmentation and achieve the desired partitioning. We validate our method using image stacks depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy.
