AI-SUITE

AI-SUITE

AI based image processing workflow

The AI-SUITE is a powerful AI image analysis tool & AI model training workflow to meet your individual image processing requirements - all specifically designed for SYNENTECs imaging and automation solutions.

It makes advanced data analysis accessible to all scientists - without any coding skills.

Discover all the advantages

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Performs...

  • fast loading of YT-SOFTWARE experiments as new AI project
  • high-precision labeling while minimizing data volume through unique algorithms
  • highly accurate, reliable image analysis
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Supports...

  • brightfield & fluorescence images
  • custom cell and feature detection
  • comprehensive options for labeling class configuration
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Offers...

  • AI and ML based project setups for demanding tasks
  • intuitive, user-friendly workflow
  • customer-specific image processing requirements

Next Level of Image Cytometry

Example of AI-based single cell detection

YT-SOFTWARE ecosYsTem broschure
AI-based single cell detection on brightfield and fluorescence_wide
AI-STUDIO+ - Case study cancer research

AI-SUITE workflow

The AI-SUITE consists of AI-STUDIO+ and AI-SERVER. If you decide to acquire a license of the AI-SUITE, the AI-STUDIO+ will be integrated as AI-Client Plug-In quite comfortably into your YT-SOFTWARE.

With the AI-STUDIO+ labeling tool, cells or ROIs can be labeled very user-friendly, which then train the corresponding ML model used in the AI-SERVER. 

Make advanced data analysis accessible to all scientists with the AI-SUITE - without coding skills:

  • Accelerate your imaging projects and get them to the project goal faster and more accurately
  • Benefit from easy-to-use next-generation machine learning segmentation and classification tools
  • Easily train, update, and apply deep learning models via local resources or the AI-STUDIO+ cloud platform
  • Use this capability to approach your individual cellular images with customized analysis

Train the model

Impressions from AI-STUDIO+ labeling tool

1) Setup project - load YT-experiment or existing labeling data. 2) Select wells – easy-to-use mouse wheel zoom-in. 3) Label objects for model selection - broad options for class config. 4) Export objects for model training - transfer to AI evaluation.
1) Setup project - load YT-experiment or existing labeling data. 2) Select wells – easy-to-use mouse wheel zoom-in. 3) Label objects for model selection - broad options for class config. 4) Export objects for model training - transfer to AI evaluation.