Neuron tracking in C. elegans through automated anchor neuron localization and segmentation

Abstract

Tracking fluorescent objects through movies is a critical first step in quantifying electrical or molecular dynamics in cells. In many applications, it is necessary to track large numbers of fluorescent objects moving through tissue in a nonrigid manner. In this submission, we describe the use of a graph attention-based neural network to detect-and-link fluorescent neuronal nuclei in the brain of freely behaving worms (C. elegans). This approach allows us to reliably match on average 33% of the cells. When combined with a nonrigid registration algorithm that can leverage partial matches, this approach allows efficient tracking of all cells with substantially less manual intervention. Further work is needed to integrate this into previous registration pipelines.

Deep Neural Networks to Register and Annotate the Cells of theC. elegansNervous System

“…Datasets from freely-moving animals pose an especially challenging case. Methods for aligning cells across timepoints in moving datasets include approaches that link neurons across adjacent timepoints [21][22][23] , as well as approaches that use signal demixing 24 , alignment of body position markers using anatomical constraints 25,26 , or registration/clustering/matching based on features of the neurons, such as their centroid positions [27][28][29][30][31][32] . Targeted data augmentation combined with deep learning applied to raw images has recently been used to reduce manual labeling time during cell alignment.…”