Reconstructing neuronal anatomy from whole-brain images

Gornet, J., Venkataraju, K. U., Narasimhan, A., Turner, N., Lee, K., Seung, H. S., Osten, P., Sumbul, U. (April 2019) Reconstructing neuronal anatomy from whole-brain images. Proceedings - International Symposium on Biomedical Imaging, 218-222 art no 8759197. ISBN 19457928 (ISSN); 9781538636411 (ISBN)

DOI: 10.1109/ISBI.2019.8759197


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\times 0.4\times 2.5\mu \mathrm{m}^{3}. 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. © 2019 IEEE.

Item Type: Book
Subjects: Investigative techniques and equipment > microscopy > flourescence microscopy
Investigative techniques and equipment > Whole Brain Circuit Mapping
CSHL Authors:
Depositing User: Matthew Dunn
Date: April 2019
Date Deposited: 08 Nov 2019 17:19
Last Modified: 08 Nov 2019 17:19
PMCID: 2019

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