  Recent advances in brain clearing and imaging have made it possible to image
entire mammalian brains at sub-micron resolution. These images offer the
potential to assemble brain-wide atlases of neuron morphology, but manual
neuron reconstruction remains a bottleneck. Several automatic reconstruction
algorithms exist, but most focus on single neuron images. In this paper, we
present a probabilistic reconstruction method, ViterBrain, which combines a
hidden Markov state process that encodes neuron geometry with a random field
appearance model of neuron fluorescence. Our method utilizes dynamic
programming to compute the global maximizers of what we call the "most
probable" neuron path. Our most probable estimation method models the task of
reconstructing neuronal processes in the presence of other neurons, and thus is
applicable in images with several neurons. Our method operates on image
segmentations in order to leverage cutting edge computer vision technology. We
applied our algorithm to imperfect image segmentations where false negatives
severed neuronal processes, and showed that it can follow axons in the presence
of noise or nearby neurons. Additionally, it creates a framework where users
can intervene to, for example, fit start and endpoints. The code used in this
work is available in our open-source Python package brainlit.
