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.
@article{arxiv.1903.07027,
title = {Reconstructing neuronal anatomy from whole-brain images},
author = {James Gornet and Kannan Umadevi Venkataraju and Arun Narasimhan and Nicholas Turner and Kisuk Lee and H. Sebastian Seung and Pavel Osten and Uygar Sümbül},
journal= {arXiv preprint arXiv:1903.07027},
year = {2019}
}
Comments
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)