Reconstructing neuronal anatomy from whole-brain images
This work addresses the need for efficient and accurate neuronal reconstruction from whole-brain images for neuroscience researchers, representing an incremental improvement with specific technical advancements.
The paper tackles the problem of reconstructing neuronal anatomy from whole-brain images, which is challenging due to artifacts from rapid imaging, and presents connectivity-preserving methods and data augmentation strategies using neural networks, resulting in an end-to-end automated tracing pipeline that scales to large datasets.
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.