Small flexible automated system for monitoring Caenorhabditis elegans lifespan based on active vision and image processing techniques

Abstract

Traditionally Caenorhabditis elegans lifespan assays are performed by manually inspecting nematodes with a dissection microscope, which involves daily counting of live/dead worms cultured in Petri plates for 21–25 days. This manual inspection requires the screening of hundreds of worms to ensure statistical robustness, and is therefore a time-consuming approach. In recent years, various automated artificial vision systems have been reported to increase the throughput, however they usually provide less accurate results than manual assays. The main problems identified when using these vision systems are the false positives and false negatives, which occur due to culture media changes, occluded zones, dirtiness or condensation of the Petri plates. In this work, we developed and described a new C. elegans monitoring machine, SiViS, which consists of a flexible and compact platform design to analyse C. elegans cultures using the standard Petri plates seeded with E. coli. Our system uses an active vision illumination technique and different image-processing pipelines for motion detection, both previously reported, providing a fully automated image processing pipeline. In addition, this study validated both these methods and the feasibility of the SiViS machine for lifespan experiments by comparing them with manual lifespan assays. Results demonstrated that the automated system yields consistent replicates (p-value log rank test 0.699), and there are no significant differences between automated system assays and traditionally manual assays (p-value 0.637). Finally, although we have focused on the use of SiViS in longevity assays, the system configuration is flexible and can, thus, be adapted to other C. elegans studies such as toxicity, mobility and behaviour.

Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification

“…Images were captured using the monitoring system developed in [ 29 ]. This system uses the active backlight illumination method proposed in [ 28 ], which consists of placing an RGB Raspberry Pi camera v1.3 (OmniVision OV5647, which has a resolution of 2592 × 1944 pixels, a pixel size of 1.4 × 1.4 μm, a view field of 53.50° × 41.41°, optical size of 1/4′′, and focal ratio of 2.9) in front of the lighting system (a 7′′ Raspberry Pi display 800 × 480 at a resolution at 60 fps, 24 bit RGB colour) and the inspected plate in between.…”

Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm

“…The image acquisition process was performed using the capture system [ 37 ]. To use this system, first, a laboratory operator removed the plates from the incubator, then the plates were analyzed to find condensation on the covers, if so, they were removed, otherwise, the image sequence was captured.…”

“…To use this system, first, a laboratory operator removed the plates from the incubator, then the plates were analyzed to find condensation on the covers, if so, they were removed, otherwise, the image sequence was captured. The system [ 37 ] is automatic and captures an image every second. Escherichia coli ( E. coli ) strain OP50 was placed in the center of the plate to capture the worms inside the Petri dish and not scaling the edges or near them.…”

“…The image sequence was acquired at a resolution of 1944 × 1944 pixels and a frequency of 1 Hz (1 image per second) using the system [ 37 ] ( Table 2 ).…”

“…This image acquisition system is open hardware and its assembly procedure, parts and description are described in detail in another work [ 37 ]. Worm tracking, using these image acquisition conditions, is a very challenging problem.…”