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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 |