The nematode Caenorhabditis elegans is a well-known model organism used to
investigate fundamental questions in biology. Motility assays of this small
roundworm are designed to study the relationships between genes and behavior.
Commonly, motility analysis is used to classify nematode movements and
characterize them quantitatively. Over the past years, C. elegans' motility has
been studied across a wide range of environments, including crawling on
substrates, swimming in fluids, and locomoting through microfluidic substrates.
However, each environment often requires customized image processing tools
relying on heuristic parameter tuning. In the present study, we propose a novel
Multi-Environment Model Estimation (MEME) framework for automated image
segmentation that is versatile across various environments. The MEME platform
is constructed around the concept of Mixture of Gaussian (MOG) models, where
statistical models for both the background environment and the nematode
appearance are explicitly learned and used to accurately segment a target
nematode. Our method is designed to simplify the burden often imposed on users;
here, only a single image which includes a nematode in its environment must be
provided for model learning. In addition, our platform enables the extraction
of nematode `skeletons' for straightforward motility quantification. We test
our algorithm on various locomotive environments and compare performances with
an intensity-based thresholding method. Overall, MEME outperforms the
threshold-based approach for the overwhelming majority of cases examined.
Ultimately, MEME provides researchers with an attractive platform for C.
elegans' segmentation and `skeletonizing' across a wide range of motility
assays.

本文介绍了一种新型的自动化图像分割方法，该方法称为多环境模型估计（MEME）框架，能够在各种环境下实现的多功能性，并且可以轻松地提取线虫的骨架以进行轨迹分析，相较于基于阈值的方法，该算法在几乎所有受测环境中的性能都表现出更好的表现，可为 C.elegans 的分割和骨架重建提供一个有吸引力的平台。