EVICAN - A balanced dataset for algorithm development in cell and nucleus segmentation

Mischa Schwendy, Ronald E. Unger, Sapun H. Parekh

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Motivation: Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. Results: We present a new dataset, EVICAN - Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development.

Original languageEnglish
Pages (from-to)3863-3870
Number of pages8
Issue number12
StatePublished - 31 Mar 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the Max Planck Graduate Center, the Welch Foundation [F-2008-20190330] and the Human Frontiers in Science Program [RGP0045/2018].

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press.


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