Escobar-Benavides Santiago, García-Garví Antonio, Layana-Castro Pablo E, Sánchez-Salmerón Antonio-José
Instituto de Automática e Informática Industrial, Camino de Vera S/N, Valencia, 46022, Spain.
Comput Struct Biotechnol J. 2023 Oct 4;21:4914-4922. doi: 10.1016/j.csbj.2023.09.039. eCollection 2023.
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. elegans detection algorithm, as previous work only focused on dataset-specific detection, tailored exclusively to the characteristics and appearance of the images in a given dataset. The main aim of our study is to achieve a solution that allows for robust detection, regardless of the image-capture system used, with the potential to serve as a basis for the automation of numerous assays. These potential applications include worm counting, worm tracking, motion detection and motion characterization. To train this model, a dataset consisting of a wide variety of appearances adopted by has been curated and dataset augmentation methods have been proposed and evaluated, including synthetic image generation. The results show that the model achieves an average precision of 89.5% for a wide variety of appearances that were not used during training, thereby validating its generalization capabilities.
线虫（秀丽隐杆线虫）对于神经退行性疾病、衰老和药物筛选的研究具有重要意义。然而，手动进行这些检测是一个繁琐且耗时的过程。本文提出了一种方法来实现通用的秀丽隐杆线虫检测算法，因为之前的工作仅专注于特定数据集的检测，完全是根据给定数据集中图像的特征和外观量身定制的。我们研究的主要目的是实现一种解决方案，无论使用何种图像捕获系统，都能进行可靠的检测，并有可能作为众多检测自动化的基础。这些潜在应用包括线虫计数、线虫追踪、运动检测和运动特征描述。为了训练这个模型，已经精心策划了一个由秀丽隐杆线虫呈现的各种外观组成的数据集，并提出并评估了数据集增强方法，包括合成图像生成。结果表明，该模型对于训练期间未使用的各种秀丽隐杆线虫外观实现了89.5%的平均精度，从而验证了其泛化能力。
