Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Image Analysis for Spatial Transcriptomics
"Segment cells in my tissue image" → Extract image features, segment nuclei/cells, and compute morphological features from H&E or immunofluorescence images paired with spatial data.
Python: squidpy.im.process(), squidpy.im.segment() with Cellpose backend
Extract features and segment tissue images in spatial transcriptomics data.
Required Imports
import squidpy as sq
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, filters, segmentation
Access Tissue Images
# Get image from Visium data
library_id = list(adata.uns['spatial'].keys())[0]
img_dict = adata.uns['spatial'][library_id]['images']
# High and low resolution images
hires = img_dict['hires']
lowres = img_dict['lowres']
print(f'Hires shape: {hires.shape}')
print(f'Lowres shape: {lowres.shape}')
# Get scale factors
scalef = adata.uns['spatial'][library_id]['scalefactors']
spot_diameter = scalef['spot_diameter_fullres']
hires_scale = scalef['tissue_hires_scalef']
Create ImageContainer
Goal: Wrap tissue images in Squidpy's ImageContainer for structured access and feature extraction.
Approach: Initialize an ImageContainer from the AnnData image data or a TIFF file.
# Squidpy's ImageContainer for organized image handling
img = sq.im.ImageContainer(adata.uns['spatial'][library_id]['images']['hires'])
print(img)
# Or load from file
img = sq.im.ImageContainer('tissue_image.tif')
# Access the image array
arr = img['image'].values
Extract Image Features per Spot
Goal: Compute image-derived features (summary statistics, texture) for each spatial spot.
Approach: Use Squidpy's calculate_image_features to extract per-spot features from the tissue image within each spot's footprint.
# Calculate image features for each spot
sq.im.calculate_image_features(
adata,
img,
features=['summary', 'histogram', 'texture'],
key_added='img_features',
spot_scale=1.0, # Fraction of spot diameter
n_jobs=4,
)
# Features stored in adata.obsm['img_features']
print(f"Image features shape: {adata.obsm['img_features'].shape}")
Available Image Features
# Summary statistics
sq.im.calculate_image_features(adata, img, features='summary')
# Mean, std, etc. per channel
# Histogram features
sq.im.calculate_image_features(adata, img, features='histogram', features_kwargs={'histogram': {'bins': 16}})
# Intensity distribution
# Texture features (GLCM)
sq.im.calculate_image_features(adata, img, features='texture')
# Contrast, homogeneity, correlation, ASM
# Custom features
sq.im.calculate_image_features(
adata, img,
features=['summary', 'texture'],
features_kwargs={
'summary': {'quantiles': [0.1, 0.5, 0.9]},
'texture': {'distances': [1, 2], 'angles': [0, np.pi/4, np.pi/2]},
}
)
Segment Cells/Nuclei
Goal: Segment individual cells or nuclei from tissue images using classical methods.
Approach: Apply watershed segmentation through Squidpy's segment method on a selected image channel.
# Segment using watershed
sq.im.segment(
img,
layer='image',
method='watershed',
channel=0, # Use first channel
thresh=0.5,
)
# Access segmentation mask
seg_mask = img['segmented_watershed'].values
Segment with Cellpose
Goal: Perform deep learning-based cell segmentation for higher accuracy than classical methods.
Approach: Use Cellpose's pretrained nuclei model to detect and label individual cells in the tissue image.
# Cellpose provides better cell segmentation
from cellpose import models
# Load model
model = models.Cellpose(model_type='nuclei')
# Get image array
image = img['image'].values[:, :, 0] # Single channel
# Segment
masks, flows, styles, diams = model.eval(image, diameter=30, channels=[0, 0])
# Add to ImageContainer
img.add_img(masks, layer='cellpose_masks')
Extract Spot Image Crops
# Get image crop around each spot
def get_spot_crop(adata, img_arr, spot_idx, crop_size=100):
coords = adata.obsm['spatial'][spot_idx]
scalef = adata.uns['spatial'][library_id]['scalefactors']['tissue_hires_scalef']
x, y = int(coords[0] * scalef), int(coords[1] * scalef)
half = crop_size // 2
crop = img_arr[max(0, y-half):y+half, max(0, x-half):x+half]
return crop
# Get crop for spot 0
crop = get_spot_crop(adata, hires, 0)
plt.imshow(crop)
Color Deconvolution (H&E)
Goal: Separate hematoxylin and eosin stain channels from an H&E tissue image.
Approach: Convert RGB to HED color space using scikit-image, then extract individual stain channels.
