bio-imaging-mass-cytometry-cell-segmentation

Version Compatibility

Reference examples tested with: Cellpose 3.0+, anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, steinbock 0.16+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Cell Segmentation for IMC

"Segment cells from my IMC images" → Identify individual cell boundaries in multiplexed imaging data using deep learning (Cellpose) or watershed-based approaches for single-cell extraction.

• Python: cellpose.models.Cellpose() for deep learning segmentation
• CLI: steinbock segment for pipeline-based segmentation

Cellpose Segmentation

from cellpose import models, io
import numpy as np
import tifffile

# Load image
img = tifffile.imread('processed.tiff')

# 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)

# Run segmentation
masks, flows, styles, diams = model.eval(
    nuclear_channel,
    diameter=30,  # Average nucleus diameter in pixels
    flow_threshold=0.4,
    cellprob_threshold=0.0
)

# masks contains integer labels for each cell
print(f'Cells segmented: {masks.max()}')

Whole-Cell Segmentation with Cellpose

# Use membrane marker for whole-cell
membrane_channel = img[1]  # e.g., CD45

# Combine nuclear and membrane for cyto model
model = models.Cellpose(model_type='cyto2', gpu=True)

# Create 2-channel input [membrane, nuclear]
img_input = np.stack([membrane_channel, nuclear_channel])

masks, flows, styles, diams = model.eval(
    img_input,
    channels=[1, 2],  # [membrane, nuclear]
    diameter=50,
    flow_threshold=0.4
)

Mesmer (DeepCell)

from deepcell.applications import Mesmer

# Initialize Mesmer
app = Mesmer()

# Prepare input: (batch, H, W, 2) - [nuclear, membrane]
img_input = np.stack([nuclear_channel, membrane_channel], axis=-1)
img_input = np.expand_dims(img_input, axis=0)

# Segment
predictions = app.predict(
    img_input,
    image_mpp=1.0,  # Microns per pixel
    compartment='whole-cell'  # or 'nuclear'
)

masks = predictions[0, :, :, 0]

steinbock Segmentation

# Using steinbock with Cellpose
steinbock segment cellpose \
    --img processed \
    --model cyto2 \
    --channelwise \
    --nuclear-channel 0 \
    --membrane-channel 1 \
    -o masks

# Using steinbock with DeepCell
steinbock segment deepcell \
    --img processed \
    --nuclear-channel 0 \
    --membrane-channel 1 \
    -o masks

Extract Single-Cell Data

Goal: Convert a segmented cell mask and multi-channel image stack into a per-cell expression matrix suitable for downstream phenotyping and spatial analysis.

Approach: Iterate over regionprops of the label mask, compute mean intensity per channel within each cell's pixels, and collect morphological features (area, centroid, eccentricity) into a structured DataFrame.

from skimage import measure
import pandas as pd

def extract_single_cell_data(img, masks, channel_names):
    '''Extract mean intensity per cell per channel'''

    # Region properties
    props = measure.regionprops(masks)

    # Cell info
    cell_data = []
    intensities = []

    for prop in props:
        # Basic properties
        cell_info = {
            'cell_id': prop.label,
            'area': prop.area,
            'centroid_x': prop.centroid[1],
            'centroid_y': prop.centroid[0],
            'eccentricity': prop.eccentricity
        }
        cell_data.append(cell_info)

        # Mean intensity per channel
        cell_mask = masks == prop.label
        cell_intensities = [img[c][cell_mask].mean() for c in range(len(channel_names))]
        intensities.append(cell_intensities)

    cell_df = pd.DataFrame(cell_data)
    intensity_df = pd.DataFrame(intensities, columns=channel_names)

    return cell_df, intensity_df

cell_info, intensities = extract_single_cell_data(img, masks, channel_names)
print(f'Extracted data for {len(cell_info)} cells')

Quality Control

import matplotlib.pyplot as plt

def qc_segmentation(img, masks, nuclear_channel_idx=0):
    '''Visualize segmentation quality'''

    fig, axes = plt.subplots(1, 3, figsize=(15, 5))

    # Nuclear channel
    axes[0].imshow(img[nuclear_channel_idx], cmap='gray')
    axes[0].set_title('Nuclear Channel')

    # Segmentation masks
    axes[1].imshow(masks, cmap='tab20')
    axes[1].set_title(f'Segmentation ({masks.max()} cells)')

    # Overlay
    axes[2].imshow(img[nuclear_channel_idx], cmap='gray')
    axes[2].contour(masks, colors='red', linewidths=0.5)
    axes[2].set_title('Overlay')

    for ax in axes:
        ax.axis('off')

    plt.tight_layout()
    plt.savefig('segmentation_qc.png', dpi=150)
    plt.close()

    # Statistics
    props = measure.regionprops(masks)
    areas = [p.area for p in props]

    print(f'Cells: {len(props)}')
    print(f'Area: mean={np.mean(areas):.1f}, median={np.median(areas):.1f}')

qc_segmentation(img, masks)

Expand Nuclei to Cells

from skimage.segmentation import expand_labels

# If only nuclear segmentation available, expand to approximate cells
nuclear_masks = masks  # From nuclear segmentation
expanded_masks = expand_labels(nuclear_masks, distance=10)

print(f'Expanded masks from nuclei')

Save Results

import tifffile

# Save masks as labeled image
tifffile.imwrite('cell_masks.tiff', masks.astype(np.uint16))

# Save single-cell data
cell_info.to_csv('cell_info.csv', index=False)
intensities.to_csv('cell_intensities.csv', index=False)

# Create combined AnnData
import anndata as ad

adata = ad.AnnData(X=intensities.values)
adata.var_names = channel_names
adata.obs = cell_info

# Add spatial coordinates
adata.obsm['spatial'] = cell_info[['centroid_x', 'centroid_y']].values

adata.write('imc_segmented.h5ad')

Related Skills

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