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ClawBio cell-detection

install
source · Clone the upstream repo
git clone https://github.com/ClawBio/ClawBio
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ClawBio/ClawBio "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/cell-detection" ~/.claude/skills/clawbio-clawbio-cell-detection && rm -rf "$T"
manifest: skills/cell-detection/SKILL.md
tags
#microscopy#segmentation#cellpose#fluorescence#imaging#cell-biology
source content

🔬 Cell Segmentation

You are the cell-detection agent, a specialised ClawBio skill for cell segmentation in fluorescence microscopy images. The default backend is 

cpsam
(Cellpose 4.0); additional backends (e.g. StarDist) are planned.

Why This Exists

Manual cell counting and segmentation are slow, inconsistent, and hard to reproduce.

  • Without it: Users open ImageJ, draw ROIs by hand, export CSVs with no provenance.
  • With it: One command segments cells, extracts morphology metrics, saves an overlay figure, and writes a reproducible 
    report.md
    .
  • Why ClawBio: Fully local, no data upload, structured outputs ready for downstream analysis.

Core Capabilities

  1. Segment: Run 
    cpsam
    on any TIFF, PNG, or JPG fluorescence image
  2. Measure: Extract area, equivalent diameter, centroid, and eccentricity per cell
  3. Report: Produce 
    report.md
    , 
    {stem}_measurements.csv
    , and histogram figures

Input Formats

FormatExtensionNotes
Greyscale TIFF
.tif
, 
.tiff
H×W — passed directly
2-channel TIFF
.tif
, 
.tiff
H×W×2 — cytoplasm + nuclear, any order
3-channel TIFF
.tif
, 
.tiff
H×W×3 — H&E or fluorescence, any order
>3-channel TIFF
.tif
, 
.tiff
First 3 channels used; remainder truncated with warning
PNG / JPEG
.png
, 
.jpg
, 
.jpeg
Greyscale or RGB

Channel handling: cpsam is channel-order invariant — cytoplasm and nuclear channels can be in any order. You do not need to specify which channel is which. If you have more than 3 channels, consider omitting the extra channel or combining it with another before running.

Workflow

  1. Load image; detect greyscale vs multi-channel
  2. Prepare — pass 1–3 channels through unchanged; truncate >3 to first 3 with a warning
  3. Segment with 
    CellposeModel()
    — no 
    channels
    argument needed
  4. Metrics via 
    skimage.measure.regionprops
  5. Figures — overlay + size distribution histogram
  6. Report — 
    report.md
    + 
    {stem}_measurements.csv
    + reproducibility bundle (
    commands.sh
    , 
    environment.yml
    , 
    checksums.sha256
    )

CLI Reference

# Standard usage — greyscale or multi-channel (cpsam handles channels automatically)
python skills/cell-detection/cell_detection.py \
  --input <image.tif> --output <report_dir>

# Override diameter estimate (pixels)
python skills/cell-detection/cell_detection.py \
  --input <image.tif> --diameter 30 --output <report_dir>

# Demo (synthetic image, no user file needed)
python skills/cell-detection/cell_detection.py --demo --output /tmp/cell_detection_demo

Demo

python skills/cell-detection/cell_detection.py --demo --output /tmp/cell_detection_demo

Expected output: report.md with ~67 cells detected from a synthetic 512×512 blob image (67 blobs generated).

Algorithm / Methodology

  1. Load image with 
    tifffile
    (TIFF) or 
    PIL
    (PNG/JPG); detect ndim
  2. If >3 channels, truncate to first 3 with a warning
  3. Instantiate 
    CellposeModel(gpu=<flag>)
  4. Call 
    model.eval(img, diameter=<arg_or_None>)
    — no 
    channels
    arg (cpsam is channel-order invariant)
  5. Extract per-cell stats from 
    masks
    via 
    skimage.measure.regionprops
  6. Save 
    {stem}_measurements.csv
    , figures, 
    report.md

Key parameters:

  • Model: 
    cpsam
    (Cellpose 4.0 unified model — channel-order invariant)
  • Channels: not passed — cpsam uses the first 3 channels of the input in any order
  • Diameter: 
    None
    triggers Cellpose auto-estimation

Example Queries

  • "Segment the cells in my DAPI image"
  • "How many cells are in this microscopy image?"
  • "Run cellpose on my TIFF and give me a cell count"
  • "Segment my fluorescence image and export morphology metrics"

Output Structure

output_dir/
├── report.md
├── {stem}_measurements.csv
├── {stem}_cp_masks.tif
├── {stem}_seg.npy
├── figures/
│   ├── {stem}_cp_outlines.png
│   └── {stem}_histogram.png
└── reproducibility/
    ├── checksums.sha256
    ├── commands.sh
    └── environment.yml

Dependencies

  • cellpose>=4.0
    — cpsam model
  • tifffile
    — TIFF I/O
  • Pillow
    — PNG/JPG loading
  • numpy
    — array ops
  • matplotlib
    — figures
  • scikit-image
    — regionprops metrics

Safety

  • Local-first: no image data leaves the machine
  • Every report includes the ClawBio medical disclaimer
  • Reproducibility bundle (
    commands.sh
    , 
    environment.yml
    , 
    checksums.sha256
    ) records the exact invocation, dependencies, and output integrity

Integration with Bio Orchestrator

Trigger conditions:

  • Input is a TIFF/PNG/JPG microscopy image
  • User mentions "cellpose", "segment", "cell counting", "microscopy"

Chaining partners:

  • Future: export ROI centroids to spatial transcriptomics workflows

Citations