  Reconstruction of neuroanatomy is a fundamental problem in neuroscience.
Stochastic expression of colors in individual cells is a promising tool,
although its use in the nervous system has been limited due to various sources
of variability in expression. Moreover, the intermingled anatomy of neuronal
trees is challenging for existing segmentation algorithms. Here, we propose a
method to automate the segmentation of neurons in such (potentially
pseudo-colored) images. The method uses spatio-color relations between the
voxels, generates supervoxels to reduce the problem size by four orders of
magnitude before the final segmentation, and is parallelizable over the
supervoxels. To quantify performance and gain insight, we generate simulated
images, where the noise level and characteristics, the density of expression,
and the number of fluorophore types are variable. We also present segmentations
of real Brainbow images of the mouse hippocampus, which reveal many of the
dendritic segments.
