Reconstructing neuronal anatomy from whole-brain images
| Autor: | Gornet, James, Venkataraju, Kannan Umadevi, Narasimhan, Arun, Turner, Nicholas, Lee, Kisuk, Seung, H. Sebastian, Osten, Pavel, Sümbül, Uygar |
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| Rok vydání: | 2019 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) |
| Databáze: | arXiv |
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