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AI-based image and data analysis pipeline: PHIOme offers cutting-edge cell detection and cell movement analysis on Cellwatcher images in real-time. And it doesn't stop there: The basic parameters obtained directly during image analysis are further processed in semi- or fully automated data analysis modules offering multiparametric, high quality output without any effort or expertise required. Detect cell dynamics like growth, motility, morphology, differentiation and viability, on the fly, even without any computer science background.
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high quality
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condensed key
results
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cutting-edge
algorithms
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effortless
expert results
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multiparametric
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real-time

Automatic deep learning

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Our algorithms are the basis for our cell line analysis software. Trained with more than one million images and proofed using big datasets to ensure highest quality standards, our powerful convolutional neural networks allow you to automatically analyze multiple parameters of hundreds of cells at the same time.

Real-time cell segmentation

Our software is based on the translational power of Generative Adversarial Networks (GANs) to reliably determine the total area covered by cells. Combined with our exceptionally large fields of view, you can analyze a larger population of your cells in less time.

Single cell tracking

Our models, Region Based Convolutional Neural Networks (R-CNNs), detect a large number of individual cells in lensfree microscopy images and enable non-invasive single cell tracking over days. This lays the foundation for extensive analyses of single cell motility and migration. Left: Tracking of single cell paths. Right-top: square displacement for every cell, population average (mean square displacement MSD), and fit with anomalous diffusion equation (mean square displacement MSD) yielding the diffusion rate at different timepoints. Right-bottom: comparison with MSD calculated by taking the difference in position every hour.