Understanding Neural Pathways in Zebrafish through Deep Learning and High Resolution Electron Microscope Data

TL;DR

This paper explores using deep learning to automate the segmentation of high-resolution electron microscope images for neural pathway tracing in zebrafish, aiming to accelerate neuroscience research.

Contribution

It introduces a deep learning approach tailored for high-resolution SEM image segmentation to automate neural pathway tracing in zebrafish brain data.

Findings

Initial segmentation results show promise for automation.

Deep learning reduces manual effort significantly.

Potential to accelerate brain connectivity studies.

Abstract

The tracing of neural pathways through large volumes of image data is an incredibly tedious and time-consuming process that significantly encumbers progress in neuroscience. We are exploring deep learning's potential to automate segmentation of high-resolution scanning electron microscope (SEM) image data to remove that barrier. We have started with neural pathway tracing through 5.1GB of whole-brain serial-section slices from larval zebrafish collected by the Center for Brain Science at Harvard University. This kind of manual image segmentation requires years of careful work to properly trace the neural pathways in an organism as small as a zebrafish larva (approximately 5mm in total body length). In automating this process, we would vastly improve productivity, leading to faster data analysis and breakthroughs in understanding the complexity of the brain. We will build upon prior attempts to employ deep learning for automatic image segmentation extending methods for unconventional deep learning data.