Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain.
Sci Rep. 2020 Dec 17;10(1):22247. doi: 10.1038/s41598-020-79430-8.
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. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.
当监测秀丽隐杆线虫（C. elegans）线虫时，主要问题之一是通过自动计算机视觉系统跟踪它们的姿势。考虑到它们身体的明显灵活性以及在个体行为中可以执行的不同姿势，这是一个挑战，当虫子在移动时与其他虫子聚集在一起时，情况会变得更加复杂。这项工作通过结合一些计算机视觉技术提出了一种简单的解决方案，以帮助确定某些虫子的姿势，并在聚集或卷曲形状时识别每一个虫子。这种新方法基于距离变换函数来获得更好的虫子骨架。使用 205 个平板进行了实验，每个平板上有 10、15、30、60 或 100 条虫子，总共约有 100000 个虫子姿势。对所提出的方法与经典的骨架化方法进行了比较，发现 2196 个有问题的姿势在每个虫子的姿势预测中平均提高了 22%至 1%。
