An adaptable analysis workflow for characterization of platelet spreading and morphology. (2nd January 2021)

• Record Type: Journal Article
• Authors: Pike, Jeremy A.; Simms, Victoria A.; Smith, Christopher W.; Morgan, Neil V.; Khan, Abdullah O.; Poulter, Natalie S.; Styles, Iain B.; Thomas, Steven G.
• Is Part Of: Platelets. Volume 32:Number 1(2021)
• Page Start: 54
• Page End: 58
• Publication Date: 2021-01-02
• Subjects: Image analysis -- machine learning -- platelets -- spreading; Blood platelets -- Periodicals; Blood Platelets -- Periodicals; 615.39
• DOI: 10.1080/09537104.2020.1748588
• Languages: English
• ISSNs: 0953-7104

Abstract

Abstract: The assessment of platelet spreading through light microscopy, and the subsequent quantification of parameters such as surface area and circularity, is a key assay for many platelet biologists. Here we present an analysis workflow which robustly segments individual platelets to facilitate the analysis of large numbers of cells while minimizing user bias. Image segmentation is performed by interactive learning and touching platelets are separated with an efficient semi-automated protocol. We also use machine learning methods to robustly automate the classification of platelets into different subtypes. These adaptable and reproducible workflows are made freely available and are implemented using the open-source software KNIME and ilastik.