ZEISS ZEN AI Toolkit is an integrated software module within the ZEISS ZEN microscopy suite, designed to automate and enhance image analysis using artificial intelligence and machine learning. The toolkit provides advanced tools for denoising, segmentation, and classification of microscopy images, supporting a wide range of multi-dimensional datasets, including stacks, tiles, and multi-channel images. It features intuitive training interfaces for both segmentation and classification, allowing users to train custom deep learning models without coding expertise. The toolkit supports the import and export of models, compatibility with open-source machine learning frameworks (such as TensorFlow, ONNX, and Scikit-Learn), and seamless integration with the ZEN Image Analysis Wizard. GPU acceleration and parallel computing enable efficient processing of large datasets. The AI Toolkit is suitable for both routine and advanced research applications in life sciences and materials science, providing reproducible, high-throughput analysis and facilitating the exchange and sharing of AI models.

Key Features

AI-Powered Denoising, Segmentation, and Classification Automates key image analysis tasks using deep learning and machine learning.

• Denoising based on Noise2Void for improved signal-to-noise ratio.
• Segmentation and classification of any image type readable by ZEN.
• Supports multi-dimensional datasets, including stacks and tiled images.

Intuitive Training and Model Management Enables users to train and manage custom models without coding.

• User-friendly interfaces for segmentation and classification training.
• Import/export of models from APEER and external tools (AI-ready).
• Exchange and sharing of models via open model format.

Integration and Compatibility Seamlessly integrates with ZEN and supports open-source ML frameworks.

• Works with Python, TensorFlow, ONNX, Scikit-Learn, and Dask.
• Compatible with CZI, OME-TIFF, and third-party image formats.
• Exports segmented images to third-party software tools.

Performance and Scalability Handles large datasets efficiently with hardware acceleration.

• Parallel and GPU computing for fast processing.
• Large data handling for high-throughput workflows.

Benefits

Automated, Reproducible Image Analysis Reduces manual effort and ensures consistent results.

• Standardizes analysis across users and locations.
• Enables high-throughput, reproducible workflows.

Accessible Deep Learning for Non-Experts Empowers users without coding skills to leverage AI.

• Intuitive interfaces for model training and deployment.
• No expertise or coding required for most workflows.

Flexible and Extensible Platform Adapts to diverse research needs and integrates with existing tools.

• Supports a wide range of microscopy applications.
• Open model format and compatibility with external ML tools.