Abstract
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 >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.
Highlights
For the selection of the most-suited machine learning algorithm, twelve classifiers were compared according to their Matthews correlation coefficient (MCC) and time to build the ten-fold cross correlation model using default parameters in the software
A dataset of 20 labeled images based on all 141 attributes available in the Trainable WEKA Segmentation plug-in was evaluated (Supplementary Material Figure S1)
Using supervised learning and the addition of a size-threshold filter, we were able to train a proficient classifier for the segmentation of worms on fluorescence images
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. O’Rourke and coworkers pointed out that vital Nile red stains lysosome-related organelles of the intestine and not neutral lipid stores [15]. The wormtoolbox [30], available through cellprofiler [31], can be used for static brightfield images of adult worms in liquid culture In this process, segmentation is performed by binarization with Otus’s thresholding. Escorcia and coworkers [32] have reported a very detailed description of quantifying Nile red fluorescence from worms on microscope slide images, but their method is highly handcrafted, e.g., by manual segmentation of the worms. Fluorescent areas are quantified in the segmented images after binarization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
