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chromatin_clumps

Extracts chromatin clumps from a given image and label-map.

Note

Applies a normalization to the image before extracting chromatin clumps.

Parameters:

Name Type Description Default
img ndarray

Image to extract chromatin clumps from. Shape (H, W, 3).

 required
label ndarray

Label map of the cells/nuclei. Shape (H, W).

 None
mean float

Mean intensity of the image.

 0.0
std float

Standard deviation of the image.

 1.0
erode bool

Whether to erode the chromatin clumps after thresholding.

 True

Returns:

Name Type Description
chrom_mask ndarray

Binary mask of chromatin clumps. Shape (H, W).
chrom_areas List[int]

Areas of the chromatin clumps.
chrom_nuc_props List[float]

Chromatin to nucleus proportion.

Examples:

>>> from histolytics.data import hgsc_cancer_he, hgsc_cancer_nuclei
>>> from histolytics.utils.raster import gdf2inst
>>> from histolytics.nuc_feats.chromatin import chromatin_clumps
>>> import matplotlib.pyplot as plt
>>>
>>> # Load example data
>>> he_image = hgsc_cancer_he()
>>> nuclei = hgsc_cancer_nuclei()
>>>
>>> # Filter for a specific cell type if needed
>>> neoplastic_nuclei = nuclei[nuclei["class_name"] == "neoplastic"]
>>>
>>> # Convert nuclei GeoDataFrame to instance segmentation mask
>>> inst_mask = gdf2inst(neoplastic_nuclei, width=he_image.shape[1], height=he_image.shape[0])
>>> # Extract chromatin clumps
>>> chrom_mask, chrom_areas, chrom_nuc_props = chromatin_clumps(he_image, inst_mask)
>>>
>>> print(f"Number of nuclei analyzed: {len(chrom_areas)}")
Number of nuclei analyzed: 258
>>> print(f"Average chromatin area per nucleus: {sum(chrom_areas)/len(chrom_areas):.2f}")
Average chromatin area per nucleus: 87.34
>>> print(f"Average chromatin proportion: {sum(chrom_nuc_props)/len(chrom_nuc_props):.4f}")
Average chromatin proportion: 0.1874
>>> fig,ax = plt.subplots(1, 2, figsize=(8, 4))
>>> ax[0].imshow(chrom_mask)
>>> ax[0].set_axis_off()
>>> ax[1].imshow(he_image)
>>> ax[1].set_axis_off()
>>> fig.tight_layout()

out

Source code in src/histolytics/nuc_feats/chromatin.py 
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def chromatin_clumps(
    img: np.ndarray,
    label: np.ndarray = None,
    mean: float = 0.0,
    std: float = 1.0,
    erode: bool = True,
) -> np.ndarray:
    """Extracts chromatin clumps from a given image and label-map.

    Note:
        Applies a normalization to the image before extracting chromatin clumps.

    Parameters:
        img (np.ndarray):
            Image to extract chromatin clumps from. Shape (H, W, 3).
        label (np.ndarray):
            Label map of the cells/nuclei. Shape (H, W).
        mean (float):
            Mean intensity of the image.
        std (float):
            Standard deviation of the image.
        erode (bool):
            Whether to erode the chromatin clumps after thresholding.

    Returns:
        chrom_mask (np.ndarray):
            Binary mask of chromatin clumps. Shape (H, W).
        chrom_areas (List[int]):
            Areas of the chromatin clumps.
        chrom_nuc_props (List[float]):
            Chromatin to nucleus proportion.

    Examples:
        >>> from histolytics.data import hgsc_cancer_he, hgsc_cancer_nuclei
        >>> from histolytics.utils.raster import gdf2inst
        >>> from histolytics.nuc_feats.chromatin import chromatin_clumps
        >>> import matplotlib.pyplot as plt
        >>>
        >>> # Load example data
        >>> he_image = hgsc_cancer_he()
        >>> nuclei = hgsc_cancer_nuclei()
        >>>
        >>> # Filter for a specific cell type if needed
        >>> neoplastic_nuclei = nuclei[nuclei["class_name"] == "neoplastic"]
        >>>
        >>> # Convert nuclei GeoDataFrame to instance segmentation mask
        >>> inst_mask = gdf2inst(neoplastic_nuclei, width=he_image.shape[1], height=he_image.shape[0])
        >>> # Extract chromatin clumps
        >>> chrom_mask, chrom_areas, chrom_nuc_props = chromatin_clumps(he_image, inst_mask)
        >>>
        >>> print(f"Number of nuclei analyzed: {len(chrom_areas)}")
        Number of nuclei analyzed: 258
        >>> print(f"Average chromatin area per nucleus: {sum(chrom_areas)/len(chrom_areas):.2f}")
        Average chromatin area per nucleus: 87.34
        >>> print(f"Average chromatin proportion: {sum(chrom_nuc_props)/len(chrom_nuc_props):.4f}")
        Average chromatin proportion: 0.1874
        >>> fig,ax = plt.subplots(1, 2, figsize=(8, 4))
        >>> ax[0].imshow(chrom_mask)
        >>> ax[0].set_axis_off()
        >>> ax[1].imshow(he_image)
        >>> ax[1].set_axis_off()
        >>> fig.tight_layout()
    ![out](../../img/chrom_clump.png)
    """
    p2, p98 = np.percentile(img, (2, 98))
    img = rescale_intensity(img, in_range=(p2, p98))

    img = rgb2gray(img) * (label > 0)
    img = (img - mean) / std
    non_zero = img.ravel()
    non_zero = non_zero[non_zero > 0]

    if non_zero.size == 0:
        return np.zeros_like(label), [], []

    try:
        otsu = threshold_multiotsu(non_zero, nbins=256)
    except ValueError:
        otsu = [threshold_otsu(non_zero)]

    chrom_mask = np.zeros_like(label)
    chrom_areas = []
    chrom_nuc_props = []
    for i in np.unique(label)[1:]:
        inst = label == i
        _intensity = img * inst
        high_mask = _intensity > otsu[0]
        chrom_clump = np.bitwise_xor(inst, high_mask)
        if erode:
            chrom_clump = erosion(chrom_clump, disk(2))
        chrom_mask += chrom_clump
        chrom_area = np.sum(chrom_clump)
        chrom_areas.append(chrom_area)
        chrom_nuc_props.append(chrom_area / np.sum(inst))

    return chrom_mask, chrom_areas, chrom_nuc_props