Segment human cells (in mitosis)

In this example, we analyze a microscopy image of human cells. We use data provided by Jason Moffat 1 through CellProfiler.

1

Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, Piqani B, Eisenhaure TM, Luo B, Grenier JK, Carpenter AE, Foo SY, Stewart SA, Stockwell BR, Hacohen N, Hahn WC, Lander ES, Sabatini DM, Root DE (2006) “A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen” Cell, 124(6):1283-98. PMID: 16564017 DOI:10.1016/j.cell.2006.01.040

import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage as ndi

from skimage import (
    color, feature, filters, io, measure, morphology, segmentation, util
)
from skimage.data import image_fetcher


path = image_fetcher.fetch('data/mitosis.tif')
image = io.imread(path)

fig, ax = plt.subplots()
ax.imshow(image, cmap='gray')
ax.set_title('Microscopy image of human cells stained for nuclear DNA')
plt.show()
Microscopy image of human cells stained for nuclear DNA

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 greyscale image is contour plotting:

fig, ax = plt.subplots(figsize=(5, 5))
qcs = ax.contour(image, origin='image')
ax.set_title('Contour plot of the same raw image')
plt.show()
Contour plot of the same raw image

The contour lines are drawn at these levels:

Out:

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]

Out:

[0, 320, 270, 48, 19, 3, 1, 0]

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[0].imshow(image)
ax[0].set_title('Original')
ax[0].axis('off')
ax[1].imshow(regions)
ax[1].set_title('Multi-Otsu thresholding')
ax[1].axis('off')
plt.show()
Original, Multi-Otsu thresholding

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:

Out:

0.7847222222222222

Whoa, this can’t be! The number of dividing nuclei

print(labeled_dividing.max())

Out:

226

is overestimated, while the total number of cells

print(labeled_cells.max())

Out:

288

is underestimated.

fig, ax = plt.subplots(ncols=3, figsize=(15, 5))
ax[0].imshow(image)
ax[0].set_title('Original')
ax[0].axis('off')
ax[2].imshow(cells)
ax[2].set_title('All nuclei?')
ax[2].axis('off')
ax[1].imshow(dividing)
ax[1].set_title('Dividing nuclei?')
ax[1].axis('off')
plt.show()
Original, Dividing nuclei?, All nuclei?

Count dividing nuclei

Clearly, not all connected regions in the middle plot are dividing nuclei. On one hand, the second threshold (value of thresholds[1]) 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 neighbouring 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 (touching) cells.

To find suitable values for thresholds and filtering parameters, we proceed by dichotomy, visually and manually.

Dividing nuclei

We are left with

Out:

29

dividing nuclei in this sample.

Segment nuclei

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_maxi = feature.peak_local_max(distance, indices=False,
                                    min_distance=7)

markers = measure.label(local_maxi)

segmented_cells = segmentation.watershed(-distance, markers, mask=cells)

fig, ax = plt.subplots(ncols=2, figsize=(10, 5))
ax[0].imshow(cells, cmap='gray')
ax[0].set_title('Overlapping nuclei')
ax[0].axis('off')
ax[1].imshow(color.label2rgb(segmented_cells, bg_label=0))
ax[1].set_title('Segmented nuclei')
ax[1].axis('off')
plt.show()
Overlapping nuclei, Segmented nuclei

Out:

/home/runner/work/scikit-image/scikit-image/doc/examples/applications/plot_human_mitosis.py:178: FutureWarning: indices argument is deprecated and will be removed in version 0.20. To avoid this warning, please do not use the indices argument. Please see peak_local_max documentation for more details.
  min_distance=7)

Additionally, we may use function color.label2rgb to overlay the original image with the segmentation result, using transparency (alpha parameter).

color_labels = color.label2rgb(segmented_cells, image, alpha=0.4, bg_label=0)

fig, ax = plt.subplots(figsize=(5, 5))
ax.imshow(color_labels)
ax.set_title('Segmentation result over raw image')
plt.show()
Segmentation result over raw image

Finally, we find a total number of

print(segmented_cells.max())

Out:

294

cells in this sample. Therefore, we estimate the mitotic index to be:

print(cleaned_dividing.max() / segmented_cells.max())

Out:

0.09863945578231292

Total running time of the script: ( 0 minutes 1.571 seconds)

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