Puckering Timothy, Thompson Jake, Sathyamurthy Sushruth, Sukumar Sinduja, Shapira Tirosh, Ebert Paul
School of Biological Sciences, University of Queensland, St Lucia, QLD, 4072, Australia.
Plant Biosecurity Cooperative Research Centre, Canberra, ACT, 2617, Australia.
F1000Res. 2017 Feb 27;6:192. doi: 10.12688/f1000research.10767.3. eCollection 2017.
There has been a recent surge of interest in computer-aided rapid data acquisition to increase the potential throughput and reduce the labour costs of large scale studies. We present Automated WormScan, a low-cost, high-throughput automated system using commercial photo scanners, which is extremely easy to implement and use, capable of scoring tens of thousands of organisms per hour with minimal operator input, and is scalable. The method does not rely on software training for image recognition, but uses the generation of difference images from sequential scans to identify moving objects. This approach results in robust identification of worms with little computational demand. We demonstrate the utility of the system by conducting toxicity, growth and fecundity assays, which demonstrate the consistency of our automated system, the quality of the data relative to manual scoring methods and congruity with previously published results.
最近，人们对计算机辅助快速数据采集的兴趣激增，目的是提高大规模研究的潜在通量并降低劳动力成本。我们展示了自动蠕虫扫描系统，这是一种使用商用照片扫描仪的低成本、高通量自动化系统，它极易实施和使用，每小时能够在操作人员极少参与的情况下对数以万计的生物体进行评分，并且具有可扩展性。该方法不依赖用于图像识别的软件训练，而是利用连续扫描生成的差异图像来识别移动物体。这种方法在计算需求极小的情况下就能可靠地识别蠕虫。我们通过进行毒性、生长和繁殖力测定来证明该系统的效用，这些测定证明了我们自动化系统的一致性、相对于手动评分方法的数据质量以及与先前发表结果的一致性。
