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Cell and nucleus segmentation tools for microscopy images. Covers Cellpose, SAM-based methods, StarDist, InstanSeg, and Mesmer.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Skills for opening and driving agent-controllable visualization components in the Pantheon UI sidebar — interactive viewers the agent can open, control, and read back. Viewers: Vitessce (spatial / single- cell omics), Viv (bioimage / microscopy), volume3d (3D image volumes — MIP/ISO), spatial3d (3D spatial transcriptomics), Mol*, IGV, Gosling, Cytoscape, MSA, RDKit, phylotree, plus agent-generated apps.
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| id | cell_segmentation_index |
| name | Cell Segmentation Skills Index |
| description | Cell and nucleus segmentation tools for microscopy images. Covers Cellpose, SAM-based methods, StarDist, InstanSeg, and Mesmer. |
| tags | ["segmentation","cellpose","sam","stardist","instanseg","mesmer","nucleus","cell"] |
Instance segmentation tools for cells and nuclei in microscopy images. Use the tool selection guide below to choose the right method, then load the corresponding skill file for detailed usage.
| Goal | Recommended Tool | Speed | Tested |
|---|---|---|---|
| Best overall accuracy | Cellpose-SAM (v4.x) | Moderate (~310s/1024px CPU) | ✅ 955 cells |
| Fastest inference | InstanSeg | Fast (~7s/1024px CPU) | ✅ 586 cells |
| Low quality / noisy images | Cellpose 3 (image restoration) | Moderate | ✅ |
| Round nuclei only | StarDist | Fastest (~0.5s) | ✅ 150 cells |
| Whole-cell (nucleus + membrane) | Mesmer / DeepCell | Moderate | ⚠️ install issues |
| Interactive annotation / 3D / tracking | micro-sam | Slow | ⚠️ Python 3.10+ |
| Fully automatic, no prompts | CellSAM | Moderate | ⚠️ Python 3.10+ |
[!TIP] Start with Cellpose (default in v4.x) for most tasks. It has the best generalization. Switch to InstanSeg if speed matters or you need simultaneous nuclei + cell masks.
[!WARNING] Environment isolation is important. These tools have conflicting dependencies. Cellpose/InstanSeg use PyTorch; StarDist/Mesmer use TensorFlow; SAM-based tools need Python 3.10+. Create separate virtual environments for each tool family:
venv-cellpose: Cellpose + InstanSeg (both PyTorch)venv-stardist: StarDist (TensorFlow,numpy<2)venv-deepcell: Mesmer/DeepCell (TensorFlow, strict numpy version)venv-sam: micro-sam / CellSAM (Python 3.10+)
General-purpose cell and nucleus segmentation using Cellpose v4.x (includes Cellpose-SAM with ViT-L backbone). Image restoration, fine-tuning, and 3D segmentation.
Skill file: cellpose.md
When to use: Default choice for most segmentation tasks.
Fast cell and nucleus segmentation with dual output (nuclei + cells simultaneously). Supports multiplexed images via ChannelNet.
Skill file: instanseg.md
When to use: Speed-critical workflows, multiplexed images, QuPath integration.
Nuclear segmentation using star-convex polygon prediction. Extremely fast but assumes round/convex nuclei.
Skill file: stardist.md
When to use: Round nuclei in fluorescence images where speed matters.
Whole-cell segmentation using both nuclear and membrane markers. TissueNet-trained PanopticNet architecture.
Skill file: mesmer.md
When to use: Tissue images with both nuclear and membrane/cytoplasm markers.
Cell segmentation using SAM adaptations: CellSAM (automatic), micro-sam (interactive + 3D), SAMCell (label-free).
Skill file: sam_based.md
When to use: Interactive annotation, 3D/tracking, or label-free brightfield.
