Brightfield vs Fluorescent Staining Dataset–A Test Bed Image Set for Machine Learning based Virtual Staining

Abstract

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.

Multi-Class Segmentation of Heterogeneous Areas in Biomedical and Environmental Images Based on the Assessment of Local Edge Density

“…To validate the proposed approach, we first consider a recently reported image set containing human colon adenocarcinoma Caco-2 cells microscopy images obtained in both bright-field and fluorescent regimes (Trizna et al, 2023). In the following, we analyze the respective image series using the edge density based segmentation algorithm.…”

OrgaMeas: A pipeline that integrates all the processes of organelle image analysis

“…In the past few years, artificial intelligence technologies based on deep learning, a family of machine learning techniques, have been applied to basic biomedical research (Moen et al, 2019). In the cell biology field, deep learning has enabled significant improvements in image analysis, including in the segmentation of intracellular structures (Gallusser et al, 2023), prediction of organelle-specific fluorescence signals from unlabeled microscopy images (Trizna et al, 2023), denoising (Laine et al, 2021), and super-resolution image reconstruction (Xu et al, 2023). U-Net is a popular deep learning technique that is specifically used to develop segmentation tools for biomedical images (Ronneberger et al, 2015).…”