实现了C.琼脂上的秀丽隐杆线虫
Towards automated high-throughput screening of C. elegans on agar
摘要：
利用模式生物进行高通量筛选（HTS）是一种很有前途的方法，可用于鉴定少量与人类生物学或疾病潜在相关的基因或药物。在HTS实验中，机器人和计算机完成了很大一部分实验工作。然而，一个剩余的主要瓶颈是手动分析实验结果，这通常是在显微镜图像的形式。这种人工检查是劳动密集型的、缓慢的和主观的。在这里，我们报告了我们在应用计算机视觉和机器学习方法来分析使用秀丽隐杆线虫（C. elegans）蠕虫。我们的主要贡献是一个强大的分割算法分离的蠕虫从背景中使用明场图像。我们还表明，通过将这种分割算法的输出与检测荧光染料尼罗红的算法相结合，我们可以可靠地区分不同的基于荧光的表型，即使视觉差异是微妙的。我们的方法的准确性类似于专家人类分析师。这一新功能是使用C语言进行全自动高温超导实验的重要一步。优雅
英文摘要：
High-throughput screening (HTS) using model organisms is a promising method to identify a small number of genes or drugs potentially relevant to human biology or disease. In HTS experiments, robots and computers do a significant portion of the experimental work. However, one remaining major bottleneck is the manual analysis of experimental results, which is commonly in the form of microscopy images. This manual inspection is labor intensive, slow and subjective. Here we report our progress towards applying computer vision and machine learning methods to analyze HTS experiments that use Caenorhabditis elegans (C. elegans) worms grown on agar. Our main contribution is a robust segmentation algorithm for separating the worms from the background using brightfield images. We also show that by combining the output of this segmentation algorithm with an algorithm to detect the fluorescent dye, Nile Red, we can reliably distinguish different fluorescence-based phenotypes even though the visual differences are subtle. The accuracy of our method is similar to that of expert human analysts. This new capability is a significant step towards fully automated HTS experiments using C. elegans.
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