  Reconstructing multiple molecularly defined neurons from individual brains
and across multiple brain regions can reveal organizational principles of the
nervous system. However, high resolution imaging of the whole brain is a
technically challenging and slow process. Recently, oblique light sheet
microscopy has emerged as a rapid imaging method that can provide whole brain
fluorescence microscopy at a voxel size of 0.4 by 0.4 by 2.5 cubic microns. On
the other hand, complex image artifacts due to whole-brain coverage produce
apparent discontinuities in neuronal arbors. Here, we present
connectivity-preserving methods and data augmentation strategies for supervised
learning of neuroanatomy from light microscopy using neural networks. We
quantify the merit of our approach by implementing an end-to-end automated
tracing pipeline. Lastly, we demonstrate a scalable, distributed implementation
that can reconstruct the large datasets that sub-micron whole-brain images
produce.
