HEStain

Targets:

image

Image Types:uint8, float32

H&E stain augmentation for histopathology. method: preset, random_preset, vahadane, macenko. Simulates staining variation for robust pathology models.

This transform simulates different H&E staining conditions using either:

Predefined stain matrices (8 standard references)
Vahadane method for stain extraction
Macenko method for stain extraction
Custom stain matrices

Arguments

method

preset | random_preset | vahadane | macenko

random_preset

Method to use for stain augmentation:

"preset": Use predefined stain matrices
"random_preset": Randomly select a preset matrix each time
"vahadane": Extract using Vahadane method
"macenko": Extract using Macenko method Default: "preset"

preset

Preset stain matrix to use when method="preset":

"ruifrok": Standard reference from Ruifrok & Johnston
"macenko": Reference from Macenko's method
"standard": Typical bright-field microscopy
"high_contrast": Enhanced contrast
"h_heavy": Hematoxylin dominant
"e_heavy": Eosin dominant
"dark": Darker staining
"light": Lighter staining Default: "standard"

intensity_scale_range

tuple[float, float]

[0.7,1.3]

Range for multiplicative stain intensity variation. Values are multipliers between 0.5 and 1.5. For example:

(0.7, 1.3) means stain intensities will vary from 70% to 130%
(0.9, 1.1) gives subtle variations
(0.5, 1.5) gives dramatic variations Default: (0.7, 1.3)

intensity_shift_range

tuple[float, float]

[-0.2,0.2]

Range for additive stain intensity variation. Values between -0.3 and 0.3. For example:

(-0.2, 0.2) means intensities will be shifted by -20% to +20%
(-0.1, 0.1) gives subtle shifts
(-0.3, 0.3) gives dramatic shifts Default: (-0.2, 0.2)

augment_background

bool

false

Whether to apply augmentation to background regions. Default: False

Examples

>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Create a sample H&E stained histopathology image
>>> # For real use cases, load an actual H&E stained image
>>> image = np.zeros((300, 300, 3), dtype=np.uint8)
>>> # Simulate tissue regions with different staining patterns
>>> image[50:150, 50:150] = np.array([120, 140, 180], dtype=np.uint8)  # Hematoxylin-rich region
>>> image[150:250, 150:250] = np.array([140, 160, 120], dtype=np.uint8)  # Eosin-rich region
>>>
>>> # Example 1: Using a specific preset stain matrix
>>> transform = A.HEStain(
...     method="preset",
...     preset="standard",
...     intensity_scale_range=(0.8, 1.2),
...     intensity_shift_range=(-0.1, 0.1),
...     augment_background=False,
...     p=1.0
... )
>>> result = transform(image=image)
>>> transformed_image = result['image']
>>>
>>> # Example 2: Using random preset selection
>>> transform = A.HEStain(
...     method="random_preset",
...     intensity_scale_range=(0.7, 1.3),
...     intensity_shift_range=(-0.15, 0.15),
...     p=1.0
... )
>>> result = transform(image=image)
>>> transformed_image = result['image']
>>>
>>> # Example 3: Using Vahadane method (requires H&E stained input)
>>> transform = A.HEStain(
...     method="vahadane",
...     intensity_scale_range=(0.7, 1.3),
...     p=1.0
... )
>>> result = transform(image=image)
>>> transformed_image = result['image']
>>>
>>> # Example 4: Using Macenko method (requires H&E stained input)
>>> transform = A.HEStain(
...     method="macenko",
...     intensity_scale_range=(0.7, 1.3),
...     intensity_shift_range=(-0.2, 0.2),
...     p=1.0
... )
>>> result = transform(image=image)
>>> transformed_image = result['image']
>>>
>>> # Example 5: Combining with other transforms in a pipeline
>>> transform = A.Compose([
...     A.HEStain(method="preset", preset="high_contrast", p=1.0),
...     A.RandomBrightnessContrast(p=0.5),
... ])
>>> result = transform(image=image)
>>> transformed_image = result['image']

References

[{'description': 'A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical"', 'source': 'Analytical and quantitative cytology and histology, 2001.'}, {'description': 'M. Macenko et al., "A method for normalizing histology slides for', 'source': '2009 IEEE International Symposium on quantitative analysis," 2009 IEEE International Symposium on Biomedical Imaging, 2009.'}]

>>> import numpy as np >>> import albumentations as A >>> import cv2 >>> >>> # Create a sample H&E stained histopathology image >>> # For real use cases, load an actual H&E stained image >>> image = np.zeros((300, 300, 3), dtype=np.uint8) >>> # Simulate tissue regions with different staining patterns >>> image[50:150, 50:150] = np.array([120, 140, 180], dtype=np.uint8) # Hematoxylin-rich region >>> image[150:250, 150:250] = np.array([140, 160, 120], dtype=np.uint8) # Eosin-rich region >>> >>> # Example 1: Using a specific preset stain matrix >>> transform = A.HEStain( ... method="preset", ... preset="standard", ... intensity_scale_range=(0.8, 1.2), ... intensity_shift_range=(-0.1, 0.1), ... augment_background=False, ... p=1.0 ... ) >>> result = transform(image=image) >>> transformed_image = result['image'] >>> >>> # Example 2: Using random preset selection >>> transform = A.HEStain( ... method="random_preset", ... intensity_scale_range=(0.7, 1.3), ... intensity_shift_range=(-0.15, 0.15), ... p=1.0 ... ) >>> result = transform(image=image) >>> transformed_image = result['image'] >>> >>> # Example 3: Using Vahadane method (requires H&E stained input) >>> transform = A.HEStain( ... method="vahadane", ... intensity_scale_range=(0.7, 1.3), ... p=1.0 ... ) >>> result = transform(image=image) >>> transformed_image = result['image'] >>> >>> # Example 4: Using Macenko method (requires H&E stained input) >>> transform = A.HEStain( ... method="macenko", ... intensity_scale_range=(0.7, 1.3), ... intensity_shift_range=(-0.2, 0.2), ... p=1.0 ... ) >>> result = transform(image=image) >>> transformed_image = result['image'] >>> >>> # Example 5: Combining with other transforms in a pipeline >>> transform = A.Compose([ ... A.HEStain(method="preset", preset="high_contrast", p=1.0), ... A.RandomBrightnessContrast(p=0.5), ... ]) >>> result = transform(image=image) >>> transformed_image = result['image']