Abstract
The automation of lifespan assays with C. elegans 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 C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% ± 1.30% per plate) with respect to the manual curve, demonstrating its feasibility.
Highlights
The automation of lifespan assays with C. elegans 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
Among the assays performed with C. elegans to study ageing, one of the most outstanding is the lifespan assay [2], which consists of counting live nematodes on test plates periodically [3]
The remaining 18.82% were determined by the initial detection algorithm to be C. elegans sequences to be classified by the neural network
Summary
The automation of lifespan assays with C. elegans 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. The nematode Caenorhabditis elegans (C. elegans) has emerged as a biological model for the study of neurodegenerative diseases and ageing Their size (approximately 1 mm in length) enables their cultivation and handling in standard Petri dishes in a cost-effective way, and their transparent body makes it possible to observe their organs and tissues under a microscope. Among the assays performed with C. elegans to study ageing, one of the most outstanding is the lifespan assay [2], which consists of counting live nematodes on test plates periodically [3]. The experiment starts from the beginning of adulthood and ends when the last nematode dies Using this count, survival curves are created, representing the survival percentage of the population each day. Automation is, an attractive proposition, saving time, providing constant monitoring, and obtaining more accurate measurements
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