.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_human_mitosis.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_human_mitosis.py: ================================ 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` .. GENERATED FROM PYTHON SOURCE LINES 19-36 .. code-block:: default 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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_001.png :alt: Microscopy image of human cells stained for nuclear DNA :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 37-41 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). .. GENERATED FROM PYTHON SOURCE LINES 43-44 Another way of visualizing a grayscale image is contour plotting: .. GENERATED FROM PYTHON SOURCE LINES 44-50 .. code-block:: default 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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_002.png :alt: Contour plot of the same raw image :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 52-53 The contour lines are drawn at these levels: .. GENERATED FROM PYTHON SOURCE LINES 53-56 .. code-block:: default qcs.levels .. rst-class:: sphx-glr-script-out .. code-block:: none array([ 0., 40., 80., 120., 160., 200., 240., 280.]) .. GENERATED FROM PYTHON SOURCE LINES 57-58 Each level has, respectively, the following number of segments: .. GENERATED FROM PYTHON SOURCE LINES 58-61 .. code-block:: default [len(seg) for seg in qcs.allsegs] .. rst-class:: sphx-glr-script-out .. code-block:: none /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] .. GENERATED FROM PYTHON SOURCE LINES 62-74 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 :ref:`sphx_glr_auto_examples_segmentation_plot_multiotsu.py`. .. GENERATED FROM PYTHON SOURCE LINES 74-87 .. code-block:: default 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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_003.png :alt: Original, Multi-Otsu thresholding :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 88-92 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: .. GENERATED FROM PYTHON SOURCE LINES 92-100 .. code-block:: default cells = image > thresholds[0] dividing = image > thresholds[1] labeled_cells = measure.label(cells) labeled_dividing = measure.label(dividing) naive_mi = labeled_dividing.max() / labeled_cells.max() print(naive_mi) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.7847222222222222 .. GENERATED FROM PYTHON SOURCE LINES 101-102 Whoa, this can't be! The number of dividing nuclei .. GENERATED FROM PYTHON SOURCE LINES 102-105 .. code-block:: default print(labeled_dividing.max()) .. rst-class:: sphx-glr-script-out .. code-block:: none 226 .. GENERATED FROM PYTHON SOURCE LINES 106-107 is overestimated, while the total number of cells .. GENERATED FROM PYTHON SOURCE LINES 107-110 .. code-block:: default print(labeled_cells.max()) .. rst-class:: sphx-glr-script-out .. code-block:: none 288 .. GENERATED FROM PYTHON SOURCE LINES 111-112 is underestimated. .. GENERATED FROM PYTHON SOURCE LINES 112-125 .. code-block:: default 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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_004.png :alt: Original, Dividing nuclei?, All nuclei? :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 126-137 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 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 (touching) cells. .. GENERATED FROM PYTHON SOURCE LINES 139-141 To find suitable values for thresholds and filtering parameters, we proceed by dichotomy, visually and manually. .. GENERATED FROM PYTHON SOURCE LINES 141-156 .. code-block:: default 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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_005.png :alt: Dividing nuclei :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 157-158 We are left with .. GENERATED FROM PYTHON SOURCE LINES 158-161 .. code-block:: default cleaned_dividing = measure.label(binary_smoother_dividing) print(cleaned_dividing.max()) .. rst-class:: sphx-glr-script-out .. code-block:: none 29 .. GENERATED FROM PYTHON SOURCE LINES 162-163 dividing nuclei in this sample. .. GENERATED FROM PYTHON SOURCE LINES 165-172 Segment nuclei ============== To separate overlapping nuclei, we resort to :ref:`sphx_glr_auto_examples_segmentation_plot_watershed.py`. 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`. .. GENERATED FROM PYTHON SOURCE LINES 172-191 .. code-block:: default 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[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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_006.png :alt: Overlapping nuclei, Segmented nuclei :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_006.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 192-194 Additionally, we may use function `color.label2rgb` to overlay the original image with the segmentation result, using transparency (alpha parameter). .. GENERATED FROM PYTHON SOURCE LINES 194-202 .. code-block:: default 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() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_007.png :alt: Segmentation result over raw image :srcset: /auto_examples/applications/images/sphx_glr_plot_human_mitosis_007.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 203-204 Finally, we find a total number of .. GENERATED FROM PYTHON SOURCE LINES 204-207 .. code-block:: default print(segmented_cells.max()) .. rst-class:: sphx-glr-script-out .. code-block:: none 286 .. GENERATED FROM PYTHON SOURCE LINES 208-209 cells in this sample. Therefore, we estimate the mitotic index to be: .. GENERATED FROM PYTHON SOURCE LINES 209-211 .. code-block:: default print(cleaned_dividing.max() / segmented_cells.max()) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.10139860139860139 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.883 seconds) .. _sphx_glr_download_auto_examples_applications_plot_human_mitosis.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.22.x?filepath=notebooks/auto_examples/applications/plot_human_mitosis.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_human_mitosis.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_human_mitosis.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_