Cell Segmentation for IMC

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

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