Introduced by Da et al. in DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System
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The LIVECell (Label-free In Vitro image Examples of Cells) dataset is a large-scale microscopic image dataset for instance-segmentation of individual cells in 2D cell cultures.
5 PAPERS • 1 BENCHMARK
HeLa cells on a flat glass Dr. G. van Cappellen. Erasmus Medical Center, Rotterdam, The Netherlands
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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. 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. The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=).
HeLa cells stably expressing H2b-GFP
Glioblastoma-astrocytoma U373 cells on a polyacrylamide substrate
MDA231 human breast carcinoma cells infected with a pMSCV vector including the GFP sequence, embedded in a collagen matrix
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GFP-GOWT1 mouse stem cells
Simulated nuclei of HL60 cells stained with Hoescht
Simulated GFP-actin-stained A549 Lung Cancer cells embedded in a Matrigel matrix
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Developing Tribolium Castaneum embryo (3D cartographic projection)