README.md

metadata

title: Cell Fluorescence Quantification
emoji: 🔬
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.50.0
app_file: app.py
pinned: false
license: mit

Cell Fluorescence Quantification

Automatic detection and quantification of cytoplasmic fluorescence in RGB fluorescence microscopy images.

How it works

Each input image is assumed to be a two-channel fluorescence image stored as RGB:

• Blue channel — nuclei stain (e.g. DAPI)
• Red channel — cytoplasmic marker whose intensity we want to quantify

The pipeline is:

1. Segment nuclei from the blue channel using Gaussian smoothing → Otsu thresholding → morphological cleaning → watershed splitting on the distance transform (to separate touching nuclei).

2. Expand each nucleus outward by a fixed pixel radius to define the whole cell (nucleus + cytoplasm ring). Background pixels are assigned to their nearest nucleus, with a Voronoi-style constraint so cells never overlap.

3. Select representative cells — non-border, well-formed, spread out, with above-median cytoplasm intensity (avoids picking dim background regions).

4. Measure in the red channel:

   Cytoplasm Area    = Cell Area     - Nucleus Area
   Cytoplasm IntDen  = Cell IntDen   - Nucleus IntDen
   Mean Cytoplasm    = Cytoplasm IntDen / Cytoplasm Area
   

Outputs

• Annotated image with two concentric outlines per cell (outer = cell, inner = nucleus) and a numbered label.
• A per-cell data table with all measurements.
• A downloadable CSV of the table.

Local usage

pip install -r requirements.txt
python app.py

Then open the local URL printed in the console.

Parameters

• Cells per image — how many cells to report per image (default 5).
• Cytoplasm ring thickness — pixel radius used to expand each nucleus into the surrounding cytoplasm (default 12 px). Increase if your cells have a thicker cytoplasm ring.