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An adaptable analysis workflow for characterization of platelet spreading and morphology
Jeremy A Pike1,2, Victoria A Simms2, Christopher W Smith2
1Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham , Midlands, UK.
Platelets
|April 24, 2020
Summary
This study introduces a new workflow for analyzing platelet morphology using image analysis. The open-source tools minimize bias and automate the classification of platelet subtypes for biological research.
Area of Science:
- Hematology
- Biotechnology
- Image Analysis
Background:
- Platelet function assays are crucial in hematology.
- Quantifying platelet morphology like surface area and circularity is vital.
- Existing methods can be labor-intensive and prone to user bias.
Purpose of the Study:
- To develop a robust image analysis workflow for platelet segmentation.
- To minimize user bias in quantifying platelet parameters.
- To automate platelet subtype classification using machine learning.
Main Methods:
- Interactive machine learning for image segmentation.
- Semi-automated protocol for separating touching platelets.
- Machine learning for automated platelet subtype classification.
- Utilized open-source software KNIME and ilastik.
Main Results:
- Robust segmentation of individual platelets achieved.
- Efficient separation of touching platelets.
- Automated classification of platelets into subtypes.
- Workflow minimizes user bias and handles large cell populations.
Conclusions:
- The developed workflow offers adaptable and reproducible analysis of platelet morphology.
- Freely available open-source tools enable broader application in platelet research.
- Automated classification aids in understanding platelet heterogeneity.
