Reconstructing neuronal anatomy from whole-brain images
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 mi...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
17.03.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1903.07027 |