CellCountCV

CellCountCV performs automated cell counting in microscopy images using fully-convolutional neural networks to quantify cells and support analysis of biosensor-based experiments probing hydrogen peroxide, ATP, Ca2+, and endoplasmic reticulum stress.

Key Features:

• Fully-Convolutional Neural Networks (FCNNs): Uses FCNNs to detect and segment cells in complex microscopy images.
• Handling of complex morphologies: Detects non-convex and overlapping cellular structures that challenge traditional image processing methods.
• High accuracy: Reports an average error rate of less than 4% compared to expert estimates.
• Batch processing automation: Automates processing of large series of microscopy images for high-throughput experiments.
• Biosensor analysis capability: Applies quantification to genetically engineered biosensor systems monitoring hydrogen peroxide, ATP, Ca2+, and endoplasmic reticulum stress.
• Research applications: Enables analysis of endoplasmic reticulum stress development and dose-dependent effects of compounds such as tunicamycin.
• Computational integration: Supports image processing via Python scripts and JSON-RPC calls.

Scientific Applications:

• Quantitative cell counting: Provides precise cell counts for microscopy-based experiments.
• Biosensor assay analysis: Quantifies cellular responses in genetically encoded biosensor experiments for reactive species and ions including hydrogen peroxide, ATP, and Ca2+.
• Endoplasmic reticulum stress studies: Facilitates monitoring and quantification of ER stress dynamics and tunicamycin dose-response effects.
• Pathogenesis and drug screening: Supports studies of pathogenic mechanisms and screening of potential therapeutic agents through automated image quantification.

Methodology:

Images are analyzed using fully-convolutional neural networks trained to recognize and count cells, with processing executed via Python scripts or JSON-RPC calls as demonstrated in the Jupyter notebook "Working_with_CellCounter.ipynb".