Automated Analysis of C. elegans Fluorescence Images using SegElegans

Microscopy, particularly of the fluorescent kind, is a frequently used tool in C. elegans research. The analysis of data from microscopy experiments can, however, be quite tedious and time-consuming. Thus, automation is desirable. We developed SegElegans, a two-headed U-net-based convolutional neural network system that is specifically designed for the automated segmentation of worms, even in images with large numbers of touching or overlapping individuals. The first part of SegElegans consists of one encoder and two decoders. The encoder, based on the SmaAt AT model, applies double convolution layers followed by a Convolutional Block Attention Module (CBAM). Both decoders use convolutional LSTMs: one performs semantic segmentation of worm images (body, edge, background, or overlap), while the other extracts a linear skeleton along each worm. The second part is a post-processing algorithm that combines the outputs of the two decoders and uses them to generate accurate instance segmentations. These segmentations can then be fed to ImageJ or other appropriate image analysis tools. Here we present instructions on how to access and run this system. We provide an online, cloud computing-based implementation as well as two methods to use the SegElegans models offline, on a local machine, should the required hardware be available.