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High-Throughput Imaging

Get faster insights into drug response and effect

Discover how to perform parallel image analysis with seamless sample-to-image correlation

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35%

higher screening throughput

Automatic

sample tracking

2x

faster data processing

Tetra workflow
for high-throughput imaging
Traditional workflow
for high-throughput imaging

Automated data centralization

Centralize and securely store data while automating data preparation to increase efficiency and ensure data integrity

Easy sample tracking

Track samples in wells and link resulting images to well positions, enabling faster insights into therapeutic response and effects

Faster image analysis

Speed up analysis by automatically attributing metadata and co-locating image files with analysis applications in the cloud

Inefficient data handling

Manual transfer and preparation of large image files slow down workflows

Difficult sample tracking

Tracking samples and attributing results is tedious and error-prone

Cumbersome image analysis

Proprietary formats and missing metadata complicate image analysis

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Learn how to transform your scientific data into AI-based outcomes.

Blog
Explore key topics across life sciences, data and AI
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Case study
Learn how SOLVO and TetraScience cut scientists' work in half with AI
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Video
Learn about the paradigm shift in scientific data management and analysis
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Unlock the full value of your imaging data

Replatform

Automatically ingest native raw files from imaging systems and associate them with relevant metadata for easy search

Engineer

Automatically parse raw CZI files into smaller PNG tiles, preparing them for analytics and AI applications

Analytics

Accelerate analysis with parallel image processing driven by high-performance computing (HPC)

AI

Feed AI/ML algorithms with AI-native data for deeper insights

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Scaling up high-content imaging through engineered BioTek Gen5 data

Managing imaging datasets was a headache for bioengineers at a top synthetic biology company. It throttled screening throughput and strained resources. By deploying the Tetra Scientific Data and AI Cloud, they automated data preparation, generating AI-ready datasets at scale.

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