State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowdsourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation's cost (time, click, interaction, etc.) while preserving or even improving the annotation's quality. The annotation quality of non-experts has been investigated using IoU (intersection over union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.

最新的计算机视觉算法依赖于大型且准确注释的数据，生成这些数据昂贵，费力且费时。对于微生物图像，此任务甚至更具挑战性，因为它们需要专门的专业知识才能准确标注。先前的研究表明，众包和辅助注释工具是应对这一挑战的两个潜在解决方案。在这项工作中，我们已经开发了一个基于Web的平台，可以对图像数据进行众包注释。该平台由半自动化辅助工具提供支持，以支持非专家注释者以提高注释效率。使用不同复杂度的生物图像，分析使用和不使用辅助工具的注释器的行为。进一步来说，非专家已被要求使用该平台对肠道寄生虫的微生物图像进行注释，并与专家的注释进行比较。对结果进行定量评估，确认辅助工具可以显着降低非专家注释的成本（时间，点击，交互等），同时保留甚至提高注释的质量。非专家的注释质量已使用IoU（联合交叉），精度和查全率进行了研究；基于此分析，我们提出了一些有关如何更好地设计类似的众包和辅助平台的想法。确认辅助工具可以显着降低非专家注释的成本（时间，点击，交互等），同时保留甚至提高注释的质量。非专家的注释质量已使用IoU（联合交叉），精度和查全率进行了研究；基于此分析，我们提出了一些有关如何更好地设计类似的众包和辅助平台的想法。确认辅助工具可以显着降低非专家注释的成本（时间，点击，交互等），同时保留甚至提高注释的质量。非专家的注释质量已使用IoU（联合交叉），精度和查全率进行了研究；基于此分析，我们提出了一些有关如何更好地设计类似的众包和辅助平台的想法。