vitaminp 0.1.0

Cell and nucleus segmentation for whole slide images (H&E and MIF)

VitaminP: Cell & Nuclei Segmentation for H&E and Multiplex IF

VitaminP is a deep learning model for robust cell and nuclei segmentation in H&E and multiplex immunofluorescence (MIF) images. Supports whole slide images (WSI) with automatic resolution matching and tissue detection.

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🚀 Quick Start (30 seconds)

import torch
from vitaminp import VitaminPFlex
from vitaminp.inference import WSIPredictor

# Load model
model = VitaminPFlex(model_size='large').to('cuda')
model.load_state_dict(torch.load("checkpoints/vitamin_p_flex_large.pth"))
model.eval()

# Run inference
predictor = WSIPredictor(model=model, device='cuda')
results = predictor.predict(
    wsi_path='slide.svs',
    output_dir='results',
    branch='he_nuclei',
    save_geojson=True
)

print(f"✅ Found {results['num_detections']} nuclei in {results['processing_time']:.2f}s")

That's it! Results saved to results/ with GeoJSON annotations and visualizations.

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📦 Installation

# Clone repository
git clone https://github.com/yourusername/vitaminp.git
cd vitaminp

# Install dependencies
pip install -e .

Requirements: Python 3.8+, PyTorch 2.0+, CUDA 11.8+ (for GPU)

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📖 Basic Usage

H&E Nuclei Detection

import torch
from vitaminp import VitaminPFlex
from vitaminp.inference import WSIPredictor

# Setup model
device = 'cuda'
model = VitaminPFlex(model_size='large').to(device)
model.load_state_dict(torch.load("checkpoints/vitamin_p_flex_large_fold2_best.pth"))
model.eval()

# Create predictor
predictor = WSIPredictor(
    model=model,
    device='cuda',
    patch_size=512,
    overlap=64,
    target_mpp=0.25,      # Auto-detected from file if available
    magnification=40
)

# Run inference
results = predictor.predict(
    wsi_path='slide.svs',
    output_dir='results',
    branch='he_nuclei',
    filter_tissue=True,           # Skip background tiles
    tissue_threshold=0.1,         # 10% minimum tissue
    clean_overlaps=True,          # Remove duplicates at tile boundaries
    save_geojson=True,            # Save annotations
    save_visualization=True,      # Save overlay images
    detection_threshold=0.5,      # Binary threshold (0.5-0.8)
    min_area_um=3.0,             # Filter small artifacts (μm²)
)

print(f"✅ Found {results['num_detections']} nuclei")
print(f"   Output: {results['output_dir']}")

---

Multiplex IF (MIF) Segmentation

from vitaminp.inference import ChannelConfig

# Define channel mapping
config = ChannelConfig(
    nuclear_channel=0,           # DAPI/SYTO channel
    membrane_channel=[1, 2],     # Membrane markers
    membrane_combination='max',  # Combine channels via max projection
    channel_names={0: 'SYTO13', 1: 'Cy3', 2: 'TexasRed'}
)

# Create predictor with MIF config
predictor = WSIPredictor(
    model=model,
    device='cuda',
    mif_channel_config=config,
    target_mpp=0.5,
    magnification=20
)

# Run MIF inference
results = predictor.predict(
    wsi_path='mif_image.tif',
    output_dir='results_mif',
    branch='he_nuclei',          # Uses same model weights
    save_geojson=True,
    min_area_um=5.0
)

---

Dual Modality (H&E + MIF)

Use MIF predictions (cleaner) with H&E visualization:

from vitaminp import VitaminPDual

# Load dual model
model = VitaminPDual(model_size='base').to('cuda')
model.load_state_dict(torch.load("checkpoints/vitamin_p_dual_base.pth"))
model.eval()

# Setup predictor
predictor = WSIPredictor(
    model=model,
    device='cuda',
    mif_channel_config=config
)

# Process both modalities
results = predictor.predict(
    wsi_path='he_image.png',           # H&E image
    wsi_path_mif='mif_image.png',      # Co-registered MIF
    output_dir='results_dual',
    branches=['he_nuclei', 'he_cell', 'mif_nuclei', 'mif_cell'],
    save_geojson=True
)

# H&E results now use high-quality MIF predictions automatically!
print(f"H&E nuclei: {results['he_nuclei']['num_detections']}")
print(f"MIF nuclei: {results['mif_nuclei']['num_detections']}")

Key feature: When using dual models, H&E branches automatically use MIF predictions (better quality) while keeping H&E background for visualization.

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📊 Output Files

Running inference creates the following files:

results/
├── nuclei_detections.geojson    # QuPath-compatible annotations
├── nuclei_detections.json       # Raw instance data
├── nuclei_boundaries.png        # Visualization with contours
└── nuclei_centroids.csv         # (optional) Centroid coordinates

GeoJSON format is compatible with QuPath for interactive viewing.

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🎯 Common Recipes

Process Multiple Branches

results = predictor.predict(
    wsi_path='slide.svs',
    branches=['he_nuclei', 'he_cell'],  # Process both
    output_dir='results'
)

print(f"Nuclei: {results['he_nuclei']['num_detections']}")
print(f"Cells: {results['he_cell']['num_detections']}")

---

Override MPP (for images without metadata)

results = predictor.predict(
    wsi_path='image.png',
    mpp_override=0.25,  # Force 0.25 μm/pixel
    branch='he_nuclei'
)

---

Custom Area Filtering

results = predictor.predict(
    wsi_path='slide.svs',
    branch='he_nuclei',
    min_area_um=5.0,           # Filter nuclei < 5 μm²
    detection_threshold=0.6     # Higher threshold = fewer false positives
)

---

Batch Processing

import glob
from pathlib import Path

slides = glob.glob('slides/*.svs')

for slide_path in slides:
    slide_name = Path(slide_path).stem
    results = predictor.predict(
        wsi_path=slide_path,
        output_dir=f'results/{slide_name}',
        branch='he_nuclei',
        save_geojson=True
    )
    print(f"{slide_name}: {results['num_detections']} nuclei")

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🔧 Model Checkpoints

Download pre-trained models:

Model	Size	Modality	Download
VitaminPFlex	Large	H&E or MIF	Link
VitaminPFlex	Base	H&E or MIF	Link
VitaminPDual	Base	H&E + MIF	Link

Place checkpoints in checkpoints/ folder.

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🤔 Troubleshooting

"Out of memory" error

predictor = WSIPredictor(
    model=model,
    patch_size=512,
    overlap=32,  # Reduce from 64
    mixed_precision=True  # Enable FP16
)

No MPP in metadata

results = predictor.predict(
    wsi_path='image.png',
    mpp_override=0.25,  # Manually specify
    branch='he_nuclei'
)

Too many false positives

results = predictor.predict(
    wsi_path='slide.svs',
    detection_threshold=0.7,  # Increase from 0.5
    min_area_um=5.0,         # Filter small detections
    branch='he_nuclei'
)

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📚 Citation

If you use VitaminP in your research, please cite:

@article{vitaminp2025,
  title={VitaminP: Robust Cell Segmentation for H&E and Multiplex IF},
  author={Your Name},
  journal={arXiv},
  year={2025}
}

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📄 License

MIT License - see LICENSE file.

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🙋 Support

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📧 Email: [e-mail]

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