EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation

Abstract 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 suffi...

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Vydáno v:Bioinformatics Ročník 36; číslo 12; s. 3863 - 3870
Hlavní autoři: Schwendy, Mischa, Unger, Ronald E, Parekh, Sapun H
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 01.06.2020
Témata:
Algorithms
Cell Nucleus
Image Processing, Computer-Assisted
Microscopy
Original Papers
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Shrnutí:Abstract 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. Availability and implementation The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=). Using a Mask R-CNN implementation, we demonstrate automated segmentation of cells and nuclei from brightfield images with a mean average precision of 61.6 % at a Jaccard Index above 0.5.
Bibliografie:ObjectType-Article-1
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btaa225