Cell Detection

🔬 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 TIFF, CZI, ND2, PNG, or JPG fluorescence images
2. Measure: Extract area, equivalent diameter, centroid, and eccentricity per cell
3. Report: Produce report.md, {stem}_measurements.csv, and histogram figures
4. Execution control: GPU auto by default, with explicit --use_gpu / --use_cpu override flags

Input Formats

Channel handling: cpsam is channel-order invariant for 2D inputs — cytoplasm and nuclear channels can be in any order. For 2D segmentation, if you have more than 3 channels, the first 3 are used and the rest are truncated with a warning. For 3D segmentation (--do_3D) with --z_projection none, 4D stacks are preserved as Z×C×Y×X (no channel truncation at load time).

Workflow

1. Load image; detect greyscale vs multi-channel
2. Prepare
   • 2D mode: pass 1–3 channels through unchanged; truncate >3 to first 3 with a warning
   • 3D mode (--do_3D + --z_projection none): keep 4D volume as Z×C×Y×X
3. Segment with CellposeModel()
   • 2D mode: no explicit channel mapping needed
   • 3D multichannel mode: call with z_axis=0, channel_axis=1
   • Device mode: defaults to GPU-auto; --use_cpu forces CPU
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

# Override 4D stack Z handling (default is max projection)
python skills/cell-detection/cell_detection.py \
  --input <image.nd2> --z_projection none --do_3D --output <report_dir>

# Force CPU mode
python skills/cell-detection/cell_detection.py \
  --input <image.tif> --use_cpu --output <report_dir>

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), czifile (CZI), nd2 (ND2), or PIL (PNG/JPG); use CZI/ND2 metadata axes to assign C/Z/Y/X
2. Channel preparation:
   • 2D mode: if >3 channels, truncate to first 3 with a warning
   • 3D mode with --z_projection none: preserve 4D volume as Z×C×Y×X
3. Instantiate CellposeModel(gpu=<flag>)
4. Call model.eval(img, diameter=<arg_or_None>)
   • 2D: no channels/channel_axis needed (cpsam is channel-order invariant)
   • 3D Z×C×Y×X: pass z_axis=0, channel_axis=1
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:
  • 2D: channel-order invariant; first 3 channels are used when input has >3 channels
  • 3D with --z_projection none: multichannel 4D stacks are kept as Z×C×Y×X
• Diameter: None leaves the image at native scale; a positive value rescales objects toward Cellpose's 30-pixel training diameter
• 4D stack policy:
  • --z_projection max (default): max-project over Z while preserving channels for 2D segmentation (H×W×C)
  • --z_projection none: preserve Z; 4D stacks remain volumetric (Z×C×Y×X) for 3D segmentation
• 3D guardrails:
  • --do_3D requires volumetric input (Z×Y×X or Z×C×Y×X)
  • non-volumetric input with --do_3D falls back to 2D mode when safe, otherwise errors

Notes

• Measurements are reported in pixel units (px, px²). Physical calibration metadata (um/pixel) is not currently propagated into per-cell metrics.
• For volumetric segmentation outputs, outlines PNG is replaced with a note file ({stem}_cp_outlines_unavailable.txt) because Cellpose does not emit 3D outlines PNGs.

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
• czifile>=2019.7.2.2 — Zeiss CZI I/O (manually verified with 2019.7.2.2)
• nd2>=0.11.1 — Nikon ND2 I/O (manually verified with 0.11.1)
• 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

• Pachitariu, Rariden & Stringer (2025) Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv 2025.04.28.651001 — CellposeSAM / cpsam model