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bio-imaging-mass-cytometry-cell-segmentation

Maintainer FreedomIntelligence · Last updated April 1, 2026

Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.

OpenClawNanoClawAnalysisReproductionbio-imaging-mass-cytometry-cell-segmentation🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometrycell

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-imaging-mass-cytometry-cell-segmentation

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Python: cellpose.models.Cellpose() for deep learning segmentation.
  • CLI: steinbock segment for pipeline-based segmentation.
  • Segment cells from my IMC images" → Identify individual cell boundaries in multiplexed imaging data using deep learning (Cellpose) or watershed-based approaches for single-cell extraction. Python: cellpose.models.Cellpose() for deep learning segmentation CLI: steinbock segment for pipeline-based segmentation.
  • img = tifffile.imread('processed.tiff').

Source Doc

Excerpt From SKILL.md

Cellpose Segmentation

from cellpose import models, io
import numpy as np
import tifffile

## Extract nuclear channel (e.g., DNA1)

nuclear_channel = img[0]  # Adjust index based on panel

## Initialize Cellpose model

model = models.Cellpose(model_type='nuclei', gpu=True)

Use cases

  • Use when extracting single-cell data from IMC or MIBI images after preprocessing.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • data-preprocessing - Prepare images before segmentation
  • phenotyping - Classify segmented cells
  • spatial-analysis - Analyze cell spatial relationships

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