Automated lifespan determination for C. elegans cultured in standard Petri dishes is challenging. Problems include occlusions of Petri dish edges, aggregation of worms, and accumulation of dirt (dust spots on lids) during assays, etc. This work presents a protocol for a lifespan assay, with two image-processing pipelines applied to different plate zones, and a new data post-processing method to solve the aforementioned problems. Specifically, certain steps in the culture protocol were taken to alleviate aggregation, occlusions, contamination, and condensation problems. This method is based on an active illumination system and facilitates automated image sequence analysis, does not need human threshold adjustments, and simplifies the techniques required to extract lifespan curves. In addition, two image-processing pipelines, applied to different plate zones, were employed for automated lifespan determination. The first image-processing pipeline was applied to a wall zone and used only pixel level information because worm size or shape features were unavailable in this zone. However, the second image-processing pipeline, applied to the plate centre, fused information at worm and pixel levels. Simple death event detection was used to automatically obtain lifespan curves from the image sequences that were captured once daily throughout the assay. Finally, a new post-processing method was applied to the extracted lifespan curves to filter errors. The experimental results showed that the errors in automated counting of live worms followed the Gaussian distribution with a mean of 2.91% and a standard deviation of ±12.73% per Petri plate. Post-processing reduced this error to 0.54 ± 8.18% per plate. The automated survival curve incurred an error of 4.62 ± 2.01%, while the post-process method reduced the lifespan curve error to approximately 2.24 ± 0.55%.

秀丽隐杆线虫的自动寿命测定在标准培养皿中培养具有挑战性。问题包括培养皿边缘的堵塞，蠕虫的聚集以及化验期间灰尘的积累（盖子上的灰尘斑）等。这项工作提出了一种寿命化验的方案，将两个图像处理管线应用于不同的板区，并且解决上述问题的一种新的数据后处理方法。具体而言，在培养方案中采取了某些步骤来减轻聚集，堵塞，污染和冷凝问题。此方法基于主动照明系统，并且有助于自动图像序列分析，不需要人工调整阈值，并且简化了提取寿命曲线所需的技术。此外，两条图像处理管线分别应用于不同的印版区域，被用于自动寿命确定。第一条图像处理管道应用于墙区域，并且仅使用像素级信息，因为蠕虫的大小或形状特征在该区域不可用。但是，应用于印版中心的第二个图像处理管道在蠕虫和像素级别融合了信息。简单的死亡事件检测用于从整个分析过程中每天捕获一次的图像序列中自动获得寿命曲线。最后，将新的后处理方法应用于提取的寿命曲线以过滤误差。实验结果表明，活蠕虫自动计数的误差遵循高斯分布，每个陪替氏培养皿的平均值为2.91％，标准偏差为±12.73％。后处理可将这一误差降低到每块板0.54±8.18％。