Identify nuclei and whole cells in preprocessed IMC or MIBI tissue images using Cellpose or Mesmer. Extract marker intensities, cell shapes, and positions, inspect segmentation overlays, and save labeled images and tables for further single-cell analysis.
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Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.
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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:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
cellpose.models.Cellpose() for deep learning segmentationsteinbock segment for pipeline-based segmentationfrom 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()}')
# 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
)
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]
# 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
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')
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)
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')
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')
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