Researchers at the School of Engineering and Digital Arts, together with colleagues in the School of Biosciences have published an article entitled ‘A crowdsourcing semi-automatic image segmentation platform for cell biology‘ in the journal Computers in Biology and Medicine.
The article was written by EDA researchers Saber Mirzaee Baftia, Dr Chee Siang Ang, Dr Md. Moinul Hossaina, and Dr Gianluca Marcelli, together with School of Biosciences colleagues Marc Alemany-Fornes, and Dr Anastasios D.Tsaousis.
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, the researchers 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 of union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.
The full article is available to read online on Science Direct, here: