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Cell and nucleus segmentation tools for microscopy images. Covers Cellpose, SAM-based methods, StarDist, InstanSeg, and Mesmer.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/aristoteleo/PantheonOS --skill cell-segmentation-skills-indexコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Drive the user's Atrium desktop: open files in installed viewer apps (Viv bioimages, Vitessce/Spatial 3D omics, Mol* structures, IGV/Gosling genomics, Volume 3D, Cytoscape, MSA, PhyloTree, RDKit), read and steer ANY window — including ones the user opened — and call app backends.
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.
Panel design for DEVELOPING / dynamic systems (embryonic organs, differentiation, regeneration). The target experiment is usually a LATE / terminal stage, but the biology is a trajectory: terminal cell types are end-products of earlier lineage programs. A panel built from the target stage alone resolves terminal STATES but systematically misses the developmental REGULATORS that produced them. This workflow therefore uses TWO references — the target-stage dataset (cell-state resolution) and an independent EARLIER-stage reference (developmental origin) — combines them under an explicit budget split, and benchmarks on the target stage.
SOC 職業分類に基づく
SKILL.md を表示中
| 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.
