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|Abstract:||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 × 0.4 × 2.5 μm 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.|
|Citation:||Gornet, James, Kannan Umadevi Venkataraju, Arun Narasimhan, Nicholas Turner, Kisuk Lee, H. Sebastian Seung, Pavel Osten, and Uygar Sümbül. "Reconstructing Neuronal Anatomy from Whole-Brain Images." 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI) (2019): pp. 218-222. doi:10.1109/ISBI.2019.8759197|
|Pages:||218 - 222|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE 16th International Symposium on Biomedical Imaging (ISBI)|
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