A step-by-step protocol for automated cell viability quantification from two-channel live/dead fluorescence images using a deep learning pipeline built on a U-Net architecture with a ResNet-50 encoder pretrained on ImageNet, providing a reproducible framework adaptable to diverse cell types and imaging platforms.
Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distingui...
Amir Reza Vazifeh, C. Zeigler, S. Meyyappan et al.· 0 citations
The emergence of automated microscopy has enabled the collection of large datasets of rare biological events, accelerating discovery in biology. However, most existing methods cannot be readily integrated into microscopy set-ups or are limited to a single microscope type. We present CelFDrive, a software package that a...
Scott Brooks, Sara Toral-Pérez, Nina Pučeková et al.· Journal of Cell Science· 0 citations
Conventional hematoxylin and eosin staining is indispensable for histopathology but is time-, labor-, and resource-intensive, environmentally unsustainable, and sensitive to operator and reagent variability. We developed a two-stage transfer-learning virtual staining (VS) pipeline for generating H&E-like images from la...
Daira Viškere, Diāna Dupļevska, Ilze Matīse-VanHoutana et al.· Journal of Imaging· 0 citations
Traditional quantitative differential phase-contrast (qDPC) imaging is constrained by the need for multiple intensity measurements, which limits its application for real-time living cell monitoring. This study introduces a single-shot quantitative DPC imaging method that integrates radially asymmetric color-encoded ill...
Ying-Ju Chen, Vu Quang Huy Tran, Sunil Vyas et al.· Optics and Photonics Interna...· 0 citations
Manual annotation of microscopic images of thin tissue sections remains time-consuming step limiting the throughput of diagnostic analysis. We present a novel automated approach using the total intensity image as an input to a U-Net architecture of Convolutional Neural Network with a pretrained ResNet-34 encoder for im...
Sooyong Chae, D. Giammattei, A. Ajmal et al.· Journal of Microscopy· 0 citations
Particle tracking is a central tool in digital microscopy for quantifying micro- and nanoscale
dynamics. Selecting and validating appropriate methods remains challenging, particularly under
low signal-to-noise ratios or high particle densities and for researchers with limited computational
expertise.
In this talk, we p...
Aaron Domenzain Del Castillo Cerecer, Alex Lech, Jesco Schönfelder et al.· Emerging Topics in Artificia...· 0 citations
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