Corrigendum to "Towards generalization for detection" [Comput Struct Biotechnol J 21 (2023) 4914-4922].

[This corrects the article DOI: 10.1016/j.csbj.

Towards generalization for detection.

The nematode () is of significant interest for research into neurodegenerative diseases, aging, and drug screening. However, conducting these assays manually is a tedious and time-consuming process. This paper proposes a methodology to achieve a generalist C.

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.

Automatic segmentation of skeletons in worm aggregations using improved U-Net in low-resolution image sequences.

Pose estimation of in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other hand, have shown good results in both low-resolution and high-resolution images.

Analysis of a lifespan prediction method based on a bimodal neural network and uncertainty estimation.

In recent decades, assays with the nematode () have enabled great advances to be made in research on aging. However, performing these assays manually is a laborious task. To solve this problem, numerous assay automation techniques are being developed to increase throughput and accuracy.

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.

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.