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数据与复现单细胞与空间组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
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bio-imaging-mass-cytometry-cell-segmentation

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-imaging-mass-cytometry-cell-segmentation🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometrycell

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • Python：cellpose.models.Cellpose() ，用于 深度学习 分割。
  • CLI：steinbock segment ，用于 pipeline-based 分割。
  • Segment cells ，面向 my IMC images" → Identify individual cell boundaries in multiplexed imaging data ，使用 深度学习 (Cellpose) 或 watershed-based approaches ，用于 single-cell extraction. Python：cellpose.models.Cellpose() ，用于 深度学习 分割 CLI：steinbock segment ，用于 pipeline-based 分割。
  • img = tifffile.imread('processed.tiff')。

原始文档

SKILL.md 摘录

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)

适用场景

  • 适合在extracting single-cell data ，面向 IMC 或 MIBI images after preprocessing时使用。

不适用场景

  • Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。

上游相关技能

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

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