Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Abstract

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an AP@0.5 IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

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

“…The mean F1 value of our segmentation is 0.67. The method of Fudickar and coworkers [29] achieved an F1 value of 0.93. The worm segmentation also results in inaccurate representation of the worm size.…”

“…After image preprocessing to remove uneven illumination, segmentation is performed with a simple global thresholding approach refined by size-filters [27]. Fudickar, Bornhorst, and coworkers [28,29] have successfully trained machine-learning classifiers for brightfield images from agar plates obtained by do-it-yourself microscopes using smartphones or Raspberry Pi camera modules. The wormtoolbox [30], available through cellprofiler [31], can be used for static brightfield images of adult worms in liquid culture.…”

Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation

“…There is a large variety of open-source DIY microscopy systems. Some projects offer inexpensive alternatives to traditional microscopy [12][13][14], systems that put emphasis on portability [15] or modularity [16], allow performing fluorescence microscopy [17][18][19][20], or even use computer vision with machine learning algorithms [21][22][23]. Although the slide/coverslip setup allows for producing high quality images and is also perfectly adapted for a small number of samples, this approach tends to be limited when the experiment requires samples parallelization or automated samples preparation.…”

Estimation of Caenorhabditis Elegans Lifespan Stages Using a Dual-Path Network Combining Biomarkers and Physiological Changes

“…Lin et al presented a quantitative method for measuring physiological age in C. elegans using a convolutional neural network (CNN) [ 11 ]. Fudickar et al proposed an image acquisition system [ 12 ]. This system was used to create large datasets containing entire dishes of C. elegans .…”