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BioMed Workspace
A web-based platform for biomedical image processing that brings advanced machine learning to researchers without requiring programming expertise.
What Can You Do?
The Biomedical Image Processing Workspace provides a complete pipeline from raw microscopy data to publication-ready visualizations.
Process Microscopy Images
Whether you work with confocal microscopy, light-sheet imaging, or electron microscopy data, the workspace handles the computational heavy lifting.
Train Deep Learning Models
Train U-Net segmentation models and Noise2Void denoising models on your specific samples - no coding required. The step-by-step workflows guide you through each stage.
Generate 3D Meshes
Convert segmented image stacks into 3D surface meshes for visualization, analysis, or 3D printing. Output formats include OBJ, STL, and Three.js JSON.
Processing Modules
| Module | Description |
|---|---|
| U-Net Segmentation | Train and run semantic segmentation models |
| Deep Learning Denoising | Self-supervised denoising with N2V and autoStructN2V |
| Filter Denoising | Classical Gaussian and Non-Local Means filtering |
| Quick Annotation | Browser-based annotation tool for ground truth creation |
| Mesh Generation | Convert segmentations to 3D polygon meshes |
| 3D Visualization | Interactive mesh viewer with clipping planes |
| Image Viewer | Browse TIFF image stacks |
| File Browser | Manage your workspace files and exports |
Get Started
Overview
Step-by-step guides and best practices
Getting Started
Welcome to the Biomedical Image Processing Workspace.
Info & Help Panel
Find the information you need directly in the Workspace
For Different Research Approaches
For Practical Imaging Work
The workspace removes the technical barriers to applying machine learning to your microscopy data. Built-in test data lets you explore features before uploading your own samples. Parameters are explained in context, and sensible defaults work for most cases.
For Computational and Quantitative Work
The workspace provides a transparent ML pipeline with full access to training dynamics and results. Monitor loss curves, validation metrics, and training progress in real-time. Results export in standard formats (TIFF, PyTorch, OBJ/STL) compatible with downstream analysis.