README.md

metadata

title: CellposeCellCounter_v5
emoji: 🔬
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false

CellposeCellCounter

Cell counting and viability from a phone photograph of a hemocytometer, using a fine-tuned Cellpose-SAM model for segmentation and a single interpretable contrast threshold for viability.

How to use it

1. Photograph one 4×4 counting block through the phone adaptor.
2. Upload it and tap once in the centre of the block. The app detects the four corners and crops to it. This step is not optional — an uncropped frame covers far more than one block and the concentration would be wrong.
3. Run segmentation. Live/dead and concentration are reported together.

Segmentation

Cellpose-SAM fine-tuned on 13 hand-corrected images (1,069 annotated cells) from this imaging setup. Held-out performance on 6 further images (556 annotated cells, IoU 0.5):

model	count error	precision	recall	F1
this model	−1.1%	0.940	0.926	0.933
previous fine-tune	+40.6%	0.656	0.932	0.768
Cellpose-SAM, off the shelf	−63.7%	0.741	0.238	0.319

The dominant error of the previous model was over-segmentation of debris: correcting its output required 336 deletions against 79 additions.

Viability

Each cell's mean grey level is divided by the median grey of a background annulus around it (20 px wide, 2 px gap, neighbouring cells excluded). Cells scoring below 1.0 — no brighter than the medium surrounding them — are called dead.

The threshold is not fitted: 1.0 means the cell has lost all refractile contrast against its own local background. It was chosen from that reasoning and then tested against 1,625 hand-labelled cells. On 556 held-out cells: accuracy 0.993, AUC 0.984, and per-field viability within 0.3 percentage points of expert annotation.

Accuracy is insensitive to the annulus geometry: across widths of 8–40 px and gaps of 0–8 px, held-out accuracy varied by 0.7 points (four cells of 556).

No training data, calibration, scaler or colour information is used for viability.

Note on the minimum size filter

It is off by default and should stay off. When enabled without an explicit value it uses the 25th percentile of each image, so it always deletes a quarter of the objects however clean the field is — measured on this dataset it removed 18–25% of real cells, cut the count by the same fraction, and inflated viability by ~2 points because dead cells are the smaller ones.

Limitations

• Viability assumes brightfield imaging with an exclusion stain (live cells refractile, dead cells stained). It does not apply to fluorescence viability assays.
• The threshold is calibration-free given a segmentation convention. Changing what the mask covers — a different model, a different working resolution — shifts the measured ratio even though the cell has not changed.
• Any dark object accepted as a cell by segmentation will be called dead, so accuracy is bounded by segmentation quality.