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EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation
- Source :
- Bioinformatics
- Publication Year :
- 2020
-
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.
- Subjects :
- Statistics and Probability
Jaccard index
Computer science
Databases and Ontologies
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Biochemistry
Grayscale
03 medical and health sciences
0302 clinical medicine
Image Processing, Computer-Assisted
Segmentation
Molecular Biology
030304 developmental biology
Cell Nucleus
0303 health sciences
Ground truth
Microscopy
business.industry
Deep learning
Original Papers
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Artificial intelligence
business
Algorithm
030217 neurology & neurosurgery
Algorithms
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....ffe13aaf2fcb51f8376b353cd4790088