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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.
Bibliographical noteFunding Information:
The authors gratefully acknowledge Steve P Watson and the members of the Birmingham platelet group who provided many useful comments and advice. The work was funded by the Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Midlands, UK and the British Heart Foundation through the Chair award (CH0/03/003) to Steve P Watson and a project grant (PG/15/114/31945) to Steven G Thomas.
© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.
- Image analysis
- machine learning
ASJC Scopus subject areas
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- 1 Finished
4/07/16 → 3/07/18