Segment human cells (in mitosis)#
import matplotlib.pyplot as plt import numpy as np from scipy import ndimage as ndi from skimage import ( color, feature, filters, measure, morphology, segmentation, util ) from skimage.data import human_mitosis image = human_mitosis() fig, ax = plt.subplots() ax.imshow(image, cmap='gray') ax.set_title('Microscopy image of human cells stained for nuclear DNA') plt.show()
We can see many cell nuclei on a dark background. Most of them are smooth and have an elliptical shape. However, we can distinguish some brighter spots corresponding to nuclei undergoing mitosis (cell division).
Another way of visualizing a grayscale image is contour plotting:
The contour lines are drawn at these levels:
array([ 0., 40., 80., 120., 160., 200., 240., 280.])
Each level has, respectively, the following number of segments:
[len(seg) for seg in qcs.allsegs]
/home/runner/work/scikit-image/scikit-image/doc/examples/applications/plot_human_mitosis.py:59: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. [14000, 4731, 1057, 353, 59, 15]
Estimate the mitotic index#
Cell biology uses the mitotic index to quantify cell division and, hence, cell proliferation. By definition, it is the ratio of cells in mitosis over the total number of cells. To analyze the above image, we are thus interested in two thresholds: one separating the nuclei from the background, the other separating the dividing nuclei (brighter spots) from the non-dividing nuclei. To separate these three different classes of pixels, we resort to Multi-Otsu Thresholding.
thresholds = filters.threshold_multiotsu(image, classes=3) regions = np.digitize(image, bins=thresholds) fig, ax = plt.subplots(ncols=2, figsize=(10, 5)) ax.imshow(image) ax.set_title('Original') ax.axis('off') ax.imshow(regions) ax.set_title('Multi-Otsu thresholding') ax.axis('off') plt.show()
Since there are overlapping nuclei, thresholding is not enough to segment all the nuclei. If it were, we could readily compute a mitotic index for this sample:
Whoa, this can’t be! The number of dividing nuclei
is overestimated, while the total number of cells
Count dividing nuclei#
Clearly, not all connected regions in the middle plot are dividing nuclei.
On one hand, the second threshold (value of
thresholds) appears to be
too low to separate those very bright areas corresponding to dividing nuclei
from relatively bright pixels otherwise present in many nuclei. On the other
hand, we want a smoother image, removing small spurious objects and,
possibly, merging clusters of neighboring objects (some could correspond to
two nuclei emerging from one cell division). In a way, the segmentation
challenge we are facing with dividing nuclei is the opposite of that with
To find suitable values for thresholds and filtering parameters, we proceed by dichotomy, visually and manually.
higher_threshold = 125 dividing = image > higher_threshold smoother_dividing = filters.rank.mean(util.img_as_ubyte(dividing), morphology.disk(4)) binary_smoother_dividing = smoother_dividing > 20 fig, ax = plt.subplots(figsize=(5, 5)) ax.imshow(binary_smoother_dividing) ax.set_title('Dividing nuclei') ax.axis('off') plt.show()
We are left with
dividing nuclei in this sample.
To separate overlapping nuclei, we resort to Watershed segmentation. To visualize the segmentation conveniently, we colour-code the labelled regions using the color.label2rgb function, specifying the background label with argument bg_label=0.
distance = ndi.distance_transform_edt(cells) local_max_coords = feature.peak_local_max(distance, min_distance=7) local_max_mask = np.zeros(distance.shape, dtype=bool) local_max_mask[tuple(local_max_coords.T)] = True markers = measure.label(local_max_mask) segmented_cells = segmentation.watershed(-distance, markers, mask=cells) fig, ax = plt.subplots(ncols=2, figsize=(10, 5)) ax.imshow(cells, cmap='gray') ax.set_title('Overlapping nuclei') ax.axis('off') ax.imshow(color.label2rgb(segmented_cells, bg_label=0)) ax.set_title('Segmented nuclei') ax.axis('off') plt.show()
Additionally, we may use function color.label2rgb to overlay the original image with the segmentation result, using transparency (alpha parameter).
Finally, we find a total number of
cells in this sample. Therefore, we estimate the mitotic index to be:
Total running time of the script: (0 minutes 3.883 seconds)