Reconstructing Neuronal Anatomy from Whole-Brain Images

• Published in: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) pp. 218 - 222
• Main Authors: Gornet, James, Venkataraju, Kannan Umadevi, Narasimhan, Arun, Turner, Nicholas, Lee, Kisuk, Seung, H. Sebastian, Osten, Pavel, Sumbul, Uygar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: Biological neural networks; Image reconstruction; Image segmentation; light microscopy; machine learning; Microscopy; Neurons; Training
• ISSN: 1945-8452
• 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.