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| title: ZO-1 Network Analysis (RIS + TiJOR) | |
| emoji: 🔬 | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.44.1 | |
| app_file: app.py | |
| python_version: 3.11 | |
| tags: | |
| - gradio | |
| - bioimage | |
| - segmentation | |
| - cellpose | |
| - computer-vision | |
| ## ZO-1 Network Analysis Tool | |
| Gradio app for AI-powered segmentation and quantification of ZO-1 tight junction networks using: | |
| - RIS (Radial Integrity Score): concentric circle crossings per pixel length | |
| - TiJOR (Rectangular method): rectangle perimeter crossings per pixel length | |
| ### How to use | |
| 1. Upload a ZO-1 image (TIFF, PNG, JPG). 16-bit TIFFs are supported. | |
| 2. In Segmentation: | |
| - Adjust Cell Diameter Estimate (px) if results are off | |
| - Scale Factor 1.0 = full size (lower if slow) | |
| - AI Contour Validation can improve contours but is slower | |
| 3. In Analysis: | |
| - Pick RIS (circles) or TiJOR (rectangles) | |
| - Tune parameters (radii/rectangles, min separation) | |
| - Optionally show contours/geometry/cross-sections | |
| 4. In Export: | |
| - Download CSV or Text report; files are named `<image>_RIS.csv/.txt` or `<image>_TiJOR.csv/.txt` | |
| ### Features | |
| - Cellpose-based segmentation (GPU or CPU) | |
| - Robust TIFF handling (8/16-bit) | |
| - RIS and TiJOR analyses with visual overlays | |
| - Exports with image-based filenames | |
| ### Parameters | |
| - Segmentation: Cell Diameter (px), Scale Factor, AI Validation | |
| - RIS: κ (packing factor), min/max radius (%), number of circles, min separation (px) | |
| - TiJOR: initial/max rectangle size (%), steps, min cross-section distance (px) | |
| ### Notes | |
| - Large images can be slow; reduce Scale Factor if needed | |
| - GPU is recommended for faster segmentation | |
| ### Acknowledgements | |
| - Built with Gradio and Cellpose |