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The small and transparent nematode Caenorhabditis elegans is increasingly employed for phenotypic in vivo chemical screens. The influence of compounds on worm body fat stores can be assayed with Nile red staining and imaging. Segmentation of C. elegans from fluorescence images is hereby a primary task. In this paper, we present an image-processing workflow that includes machine-learning-based segmentation of C. elegans directly from fluorescence images and quantifies their Nile red lipid-derived fluorescence. The segmentation is based on a J48 classifier using pixel entropies and is refined by size-thresholding. The accuracy of segmentation was &gt;90% in our external validation. Binarization with a global threshold set to the brightness of the vehicle control group worms of each experiment allows a robust and reproducible quantification of worm fluorescence. The workflow is available as a script written in the macro language of imageJ, allowing the user additional manual control of classification results and custom specification settings for binarization. Our approach can be easily adapted to the requirements of other fluorescence image-based experiments with C. elegans.

Workflow for Segmentation of Caenorhabditis elegans from Fluorescence Images for the Quantitation of Lipids