%matplotlib inline

Extract segmentation features

This example shows how to extract segmentation features from the tissue image.

Features extracted from a nucleus segmentation range from the number of nuclei per image, over nuclei shapes and sizes, to the intensity of the input channels within the segmented objects. They are very interpretable features and provide valuable additional information. Segmentation features are calculated by using features = 'segmentation', which calls squidpy.im.ImageContainer.features_segmentation().

In addition to feature_name and channels we can specify the following features_kwargs:

• label_layer - name of label image layer in img.

• props - segmentation features that are calculated. See [properties]{.title-ref} in skimage.measure.regionprops_table.

See also

• Cell-segmentation for fluorescence images for more details on calculating a cell-segmentation.

• Extract image features for the general usage of squidpy.im.calculate_image_features().

import matplotlib.pyplot as plt

import squidpy as sq

First, let’s load the fluorescence Visium dataset.

img = sq.datasets.visium_fluo_image_crop()
adata = sq.datasets.visium_fluo_adata_crop()

Before calculating segmentation features, we need to first calculate a segmentation using squidpy.im.segment.

sq.im.segment(
    img=img,
    layer="image",
    layer_added="segmented_watershed",
    method="watershed",
    channel=0,
)

Now we can calculate segmentation features. Here, we will calculate the following features:

• number of nuclei label.

• mean area of nuclei area.

• mean intensity of channels 1 (anti-NEUN) and 2 (anti-GFAP) within nuclei mean_intensity.

We use mask_cicle = True to ensure that we are only extracting features from the tissue underneath each Visium spot. For more details on the image cropping, see examples_image_compute_crops.

sq.im.calculate_image_features(
    adata,
    img,
    layer="image",
    features="segmentation",
    key_added="segmentation_features",
    features_kwargs={
        "segmentation": {
            "label_layer": "segmented_watershed",
            "props": ["label", "area", "mean_intensity"],
            "channels": [1, 2],
        }
    },
    mask_circle=True,
)

The result is stored in adata.obsm['segmentation_features'].

adata.obsm["segmentation_features"].head()

	segmentation_label	segmentation_area_mean	segmentation_area_std	segmentation_ch-1_mean_intensity_mean	segmentation_ch-1_mean_intensity_std	segmentation_ch-2_mean_intensity_mean	segmentation_ch-2_mean_intensity_std
AAACGAGACGGTTGAT-1	17	174.764706	291.276810	5604.069561	3100.506862	8997.290710	177.888882
AAAGGGATGTAGCAAG-1	14	100.785714	80.946348	5034.146353	1625.737796	10376.489346	564.254124
AAATGGCATGTCTTGT-1	16	132.000000	147.241723	11527.768307	12227.308457	7725.282284	947.987907
AAATGGTCAATGTGCC-1	9	243.000000	132.341310	3581.244911	46.124320	9664.505991	1331.259644
AAATTAACGGGTAGCT-1	7	229.142857	203.573383	9038.077440	8707.493743	10922.808071	3631.149215

Use squidpy.pl.extract to plot the texture features on the tissue image or have a look at our interactive visualization tutorial to learn how to use our interactive napari plugin. Here, we show all calculated segmentation features.

# show all channels (using low-res image contained in adata to save memory)
fig, axes = plt.subplots(1, 3, figsize=(8, 4))
for i, ax in enumerate(axes):
    ax.imshow(
        adata.uns["spatial"]["V1_Adult_Mouse_Brain_Coronal_Section_2"]["images"][
            "hires"
        ][:, :, i]
    )
    ax.set_title(f"ch{i}")

# plot segmentation features
sq.pl.spatial_scatter(
    sq.pl.extract(adata, "segmentation_features"),
    color=[
        "segmentation_label",
        "segmentation_area_mean",
        "segmentation_ch-1_mean_intensity_mean",
        "segmentation_ch-2_mean_intensity_mean",
    ],
    img_cmap="gray",
    ncols=2,
)

[segmentation_label]{.title-ref} shows the number of nuclei per spot and [segmentation_area_mean]{.title-ref} the mean are of nuclei per spot. The remaining two plots show the mean intensity of channels 1 and 2 per spot. As the stains for channels 1 and 2 are specific to Neurons and Glial cells, respectively, these features show us Neuron and Glial cell dense areas.