Brightfield vs Fluorescent Staining Dataset-A Test Bed Image Set for Machine Learning based Virtual Staining.
Elena Y TriznaAleksandr M SinitcaAsya I LyanovaDiana R BaidamshinaPavel V ZelenikhinDmitrii I KaplunAirat R KayumovMikhail I BogachevPublished in: Scientific data (2023)
Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.
Keyphrases
- induced apoptosis
- cell cycle arrest
- deep learning
- machine learning
- high resolution
- optical coherence tomography
- convolutional neural network
- endothelial cells
- magnetic resonance
- oxidative stress
- high throughput
- cell death
- signaling pathway
- magnetic resonance imaging
- mass spectrometry
- high speed
- artificial intelligence
- label free
- flow cytometry
- electronic health record
- living cells
- big data