Automation of lifespan assay using a simplified domain synthetic image-based neural network training strategy.

Performing lifespan assays with () nematodes manually is a time consuming and laborious task. Therefore, automation is necessary to increase productivity. In this paper, we propose a method to automate the counting of live using deep learning.

Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard Petri dishes.

Data from manual healthspan assays of the nematode Caenorhabditis elegans (C. elegans) can be complex to quantify. The first attempts to quantify motor performance were done manually, using the so-called thrashing or body bends assay.

Multi-Tracker Based on a Modified Skeleton Algorithm.

Automatic tracking of () in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem.

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

The automation of lifespan assays with in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead classifier based on convolutional and recurrent neural networks from low-resolution image sequences.

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

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.

Improving skeleton algorithm for helping Caenorhabditis elegans trackers.

One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving.

Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type for the Death Criterion.

Nowadays, various artificial vision-based machines automate the lifespan assays of . These automated machines present wider variability in results than manual assays because in the latter worms can be poked one by one to determine whether they are alive or not. Lifespan machines normally use a "dead or alive criterion" based on nematode position or pose changes, without poking worms.

Improving lifespan automation for Caenorhabditis elegans by using image processing and a post-processing adaptive data filter.

Automated lifespan determination for C. elegans cultured in standard Petri dishes is challenging. Problems include occlusions of Petri dish edges, aggregation of worms, and accumulation of dirt (dust spots on lids) during assays, etc.

Active backlight for automating visual monitoring: An analysis of a lighting control technique for Caenorhabditis elegans cultured on standard Petri plates.

Lifespan and healthspan machines can undergo C. elegans image segmentation errors due to changes in lighting conditions, which produce non-uniform images. Most C.