University of Reading Research Data Archive

Images supporting 'Fully automated platelet differential interference contrast image analysis via deep learning'

How to cite this Dataset


This dataset supports the publication 'Fully automated platelet differential interference contrast image analysis via deep learning' submitted to the journal Scientific Reports. All data was gathered or generated at the University of Reading from 2020 to 2021.

This dataset consists of i) 120 training images used to train a convolutional neural network (CNN) to automate platelet analyses, ii) 12 test images independent from the training images to test the CNN performance, and iii) a total of 225 8-bit images (representative of 3 independent experiments, with five fields of view captured per condition) of platelets spread over three different substrates (CRP-XL, fibrinogen, and vWF) and in the presence or absence of inhibitors (dasatinib, ibrutinib and PRT-060318) or an agonist (thrombin). These inhibitors and agonist are known to impact platelet morphology, and were used to assess the CNN�s performance on morphological extremes.

The original 16-bit images with dimensions 2424x2424 were rescaled and converted to 970x970 8-bit images to reduce the file size using ImageJ. All images within this dataset are the 8-bit images.

Resource Type: Dataset
Creators: Kempster, Carly ORCID logoORCID: and Butler, George ORCID logoORCID:
Contributors: Kuznecova, Elina, Taylor, Kirk A ORCID logoORCID:, Kriek, Neline ORCID logoORCID:, Little, Gemma ORCID logoORCID:, Sowa, Marcin A, Johnson, Louise J, Gibbins, Jonathan M and Pollitt, Alice Y ORCID logoORCID:
Rights-holders: University of Reading, George Butler
Data Publisher: University of Reading
Publication Year: 2022
Data last accessed: 22 June 2024
Metadata Record URL:
Organisational units: Life Sciences > School of Biological Sciences
Participating Organisations: University of Reading
Keywords: differential interference contrast microscopy, convolutional neural network, platelets, platelet spreading, CNN, DIC
Data Availability: OPEN


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