An adaptable analysis workflow for characterization of platelet spreading and morphology.

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.

journal_name

Platelets

journal_title

Platelets

authors

Pike JA,Simms VA,Smith CW,Morgan NV,Khan AO,Poulter NS,Styles IB,Thomas SG

doi

10.1080/09537104.2020.1748588

subject

Has Abstract

pub_date

2021-01-02 00:00:00

pages

54-58

issue

1

eissn

0953-7104

issn

1369-1635

journal_volume

32

pub_type

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