SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images

Abstract

Caenorhabditis elegans is a great model for exploring organismal, cellular, and subcellular biology through optical and fluorescence microscopy, with its research applications steadily expanding. However, manual processing of numerous microscopic images is prone to errors and demands significant labor due to worms tendency to touch or cluster with each other. Here, we present a new system for segmenting whole-body instances of Caenorhabditis elegans in microscopic images (referred to as SegElegans), employing a combination of neural network architecture and conventional image processing techniques. Our method effectively overcomes previous challenges and resolves many instances of contact and overlap between worms in highly populated images in a timely manner. The results obtained show an average Intersection over Union value of 96.3% per worm and an average improvement of 6% over other existing methods for automated analysis of worm images. SegElegns is a user-friendly application for Caenorhabditis elegans segmentation that will benefit whole-worm phenotypic screenings essential for studying development, behavior, aging, and disease.