IEEE Trans Med Imaging. 2013 Oct;32(10):1791-803. doi: 10.1109/TMI.2013.2265092. Epub 2013 May 29.
We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.
我们提出了 DevStaR，这是一个自动化的计算机视觉和机器学习系统，可在高通量 (HTP) 应用中快速、准确、定量地测量秀丽隐杆线虫胚胎的活力。秀丽隐杆线虫是一种用于研究动物发育和行为的主要遗传模式生物，由于其体型小、易于培养，因此特别适合进行高通量功能基因组分析，但缺乏有效的定量表型评分方法一直是主要瓶颈。DevStaR 使用一种新颖的分层目标识别机器来解决这一挑战，该机器可以快速分割、分类和计数秀丽隐杆线虫混合阶段群体图像中每个发育阶段的动物。在这里，我们描述了 DevStaR 系统的算法设计，并展示了其在 HTP 筛选中获取的图像数据评分方面的性能。
