Status: Production (openimagedata: 100% real, deepcell: 100% real) Last Updated: 2026-01-31
graph TB
subgraph Input["📁 Input Images"]
HE[H&E TIFF<br/>RGB brightfield<br/>Morphology]
IF1[IF_CD8 TIFF<br/>Grayscale fluorescence<br/>T cells]
IF2[IF_Ki67 TIFF<br/>Grayscale fluorescence<br/>Proliferation]
MX[MxIF TIFF<br/>3-channel RGB<br/>TP53/Ki67/DAPI]
end
subgraph OID["🔧 mcp-openimagedata<br/>(5 tools, 100% real)"]
Load[Load Image]
Comp[Generate<br/>Composite]
Anno[Annotate<br/>H&E]
end
subgraph DC["🔬 mcp-deepcell<br/>(3 tools, 100% real)"]
Seg[Segment<br/>Cells]
Quant[Quantify<br/>Markers]
Viz[Generate<br/>Overlay]
end
subgraph CC["🎯 mcp-cell-classify<br/>(3 tools, 100% real)"]
Class[Classify<br/>Phenotypes]
Multi[Multi-marker<br/>Phenotyping]
PhenoViz[Phenotype<br/>Visualization]
end
subgraph Output["📊 Outputs"]
Morph[H&E Morphology<br/>Necrosis, cellularity]
Counts[Cell Counts<br/>CD8+, Ki67+]
Pheno[Phenotypes<br/>TP53+/Ki67+ co-expression]
Imgs[PNG Visualizations<br/>Composites, overlays]
end
HE --> Load
Load --> Anno
Anno --> Morph
Anno --> Imgs
IF1 --> Load
IF2 --> Load
MX --> Load
Load --> Comp
Comp --> Imgs
Load --> Seg
Seg --> Quant
Quant --> Class
Quant --> Multi
Class --> Counts
Multi --> Pheno
Multi --> PhenoViz
PhenoViz --> Imgs
Viz --> Imgs
classDef inputStyle fill:#e1f5ff,stroke:#0288d1,stroke-width:2px
classDef oidStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px
classDef dcStyle fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
classDef ccStyle fill:#e8f5e9,stroke:#388e3c,stroke-width:2px
classDef outputStyle fill:#f1f8e9,stroke:#689f38,stroke-width:2px
class Input inputStyle
class OID oidStyle
class DC dcStyle
class CC ccStyle
class Output outputStyle
- mcp-openimagedata README - Histology image processing (5 tools, 100% real)
- mcp-deepcell README - Cell segmentation and phenotyping (3 tools, 100% real)
- mcp-cell-classify README - Cell phenotype classification (3 tools, 100% real)
Imaging analysis component for histology and multiplexed immunofluorescence (MxIF) in the Precision Medicine MCP system.
Current workflows:
- H&E: Brightfield morphology assessment (chromogenic stains, RGB TIFF)
- MxIF: Fluorescence cell segmentation (fluorescent antibodies, multi-channel TIFF)
Servers:
- mcp-openimagedata (100% real - loading, registration, feature extraction, visualization)
- mcp-deepcell (100% real - segmentation + per-cell marker quantification)
- mcp-cell-classify (100% real - phenotype classification + visualization, lightweight)
| Feature | H&E | MxIF |
|---|---|---|
| Microscopy | Brightfield | Fluorescence |
| Staining | Chromogenic dyes | Fluorescent antibodies |
| Format | RGB TIFF | Grayscale (single) or Multi-channel (multiplex) |
| Purpose | Visual morphology | Quantitative protein expression |
| Analysis | Visual inspection | Automated cell segmentation |
| Servers | openimagedata ONLY | openimagedata → deepcell → cell-classify |
| Output | Annotated images | Cell counts, phenotypes |
| Example | Necrosis detection | CD8+ T cell quantification |
Key Point: H&E uses chromogenic stains (not fluorescence!) for visual assessment.
Status: ✅ 100% Real (deployed to GCP Cloud Run) URL: https://mcp-openimagedata-ondu7mwjpa-uc.a.run.app
Tools (5):
- ✅
fetch_histology_image— PIL image loading with glob fallback for partial ID matching - ✅
register_image_to_spatial— Otsu tissue detection, bbox-based affine/rigid estimation, phase-cross-correlation refinement for deformable; Visium + generic x/y CSV support - ✅
extract_image_features— LBP + GLCM texture (25 features), connected-component morphology (15 features), intensity stats + entropy (10 features); per-ROI support - ✅
generate_multiplex_composite— RGB MxIF composites (1-7 channels) - ✅
generate_he_annotation— Annotate H&E morphology with region overlays
Use cases:
- Load H&E and IF/MxIF images
- Register histology to spatial transcriptomics spot coordinates
- Extract texture, morphology, and intensity features per region
- Generate multiplex RGB composites
- Annotate H&E regions (necrosis, high cellularity)
Status: ✅ 100% Real (deployed to GCP Cloud Run) URL: https://mcp-deepcell-ondu7mwjpa-uc.a.run.app
Tools (3):
- ✅
segment_cells— DeepCell-TF nuclear/membrane segmentation, 16-bit TIFF mask output - ✅
quantify_markers— Per-cell mean/max/min intensity for multiple markers, CSV output - ✅
generate_segmentation_overlay— Cell boundary visualization overlaid on original image
Use cases:
- Segment cells from DAPI nuclear stain or membrane markers
- Quantify per-cell marker intensities for downstream classification
- Validate segmentation quality with overlay visualizations
Status: ✅ 100% Real (lightweight, no TensorFlow dependency)
Tools (3):
- ✅
classify_cell_states— Single-marker threshold classification (proliferating/quiescent/intermediate) - ✅
classify_multi_marker— Multi-marker phenotyping (e.g., Ki67+/TP53- assignments) - ✅
generate_phenotype_visualization— Color cells by marker expression (positive/negative)
Use cases:
- Classify cell phenotypes from segmentation masks + marker images
- Multi-marker co-expression analysis (Ki67+/TP53+ double-positive cells)
- Generate publication-quality phenotype visualizations
Note: Split from mcp-deepcell for lighter dependencies (~200MB vs ~2GB Docker image). Users can swap in alternative classifiers (FlowSOM, Leiden, scikit-learn).
| File | Type | Microscopy | Servers | Purpose |
|---|---|---|---|---|
| PAT001_tumor_HE_20x.tiff | H&E | Brightfield | openimagedata | Morphology assessment |
| PAT001_tumor_IF_CD8.tiff | IF (single) | Fluorescence | openimagedata + deepcell | CD8+ T cell counts |
| PAT001_tumor_IF_KI67.tiff | IF (single) | Fluorescence | openimagedata + deepcell | Ki67+ proliferation index |
| PAT001_tumor_multiplex_IF_TP53_KI67_DAPI.tiff | MxIF (3-ch) | Fluorescence | openimagedata + deepcell | Multi-marker phenotyping |
H&E Morphology:
- Necrosis: Present (pale regions)
- Cellularity: 70-80% tumor cells
- Architecture: HGSOC (papillary, high-grade)
MxIF Quantification:
- CD8+ cells: ~12 (LOW, immune exclusion)
- Ki67+ cells: ~112 (HIGH, 45% proliferation)
- TP53+/Ki67+ double-positive: ~85 cells (35%, active growth with mutation)
For users: See server READMEs above for workflow details → Run PatientOne TEST_4_IMAGING
For developers: See mcp-openimagedata README and mcp-deepcell README for tool details
- Spatial Transcriptomics - Gene expression analysis (TEST_3)
- Multiomics Integration - RNA/Protein/Phospho integration (TEST_2)
- PatientOne Workflow - Complete end-to-end workflow
See also: Main Architecture | PatientOne README