Source code for monai.apps.pathology.transforms.stain.dictionary
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# you may not use this file except in compliance with the License.
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"""
A collection of dictionary-based wrappers around the pathology transforms
defined in :py:class:`monai.apps.pathology.transforms.array`.
Class names are ended with 'd' to denote dictionary-based transforms.
"""
from __future__ import annotations
from collections.abc import Hashable, Mapping
import numpy as np
from monai.config import KeysCollection
from monai.transforms.transform import MapTransform
from .array import ExtractHEStains, NormalizeHEStains
[docs]
class NormalizeHEStainsd(MapTransform):
"""Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.NormalizeHEStains`.
Class to normalize patches/images to a reference or target image stain.
Performs stain deconvolution of the source image using the ExtractHEStains
class, to obtain the stain matrix and calculate the stain concentration matrix
for the image. Then, performs the inverse Beer-Lambert transform to recreate the
patch using the target H&E stain matrix provided. If no target stain provided, a default
reference stain is used. Similarly, if no maximum stain concentrations are provided, a
reference maximum stain concentrations matrix is used.
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
tli: transmitted light intensity. Defaults to 240.
alpha: tolerance in percentile for the pseudo-min (alpha percentile) and
pseudo-max (100 - alpha percentile). Defaults to 1.
beta: absorbance threshold for transparent pixels. Defaults to 0.15.
target_he: target stain matrix. Defaults to None.
max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).
Defaults to None.
allow_missing_keys: don't raise exception if key is missing.
"""
def __init__(
self,
keys: KeysCollection,
tli: float = 240,
alpha: float = 1,
beta: float = 0.15,
target_he: tuple | np.ndarray = ((0.5626, 0.2159), (0.7201, 0.8012), (0.4062, 0.5581)),
max_cref: tuple | np.ndarray = (1.9705, 1.0308),
allow_missing_keys: bool = False,
) -> None:
super().__init__(keys, allow_missing_keys)
self.normalizer = NormalizeHEStains(tli=tli, alpha=alpha, beta=beta, target_he=target_he, max_cref=max_cref)
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.ndarray]:
d = dict(data)
for key in self.key_iterator(d):
d[key] = self.normalizer(d[key])
return d
ExtractHEStainsDict = ExtractHEStainsD = ExtractHEStainsd
NormalizeHEStainsDict = NormalizeHEStainsD = NormalizeHEStainsd
