[BioImage Analysis]: Nuclei segmentation

[haesleinhuepf]

I would like to segment the first channel of this image using Voronoi-Otsu-Labeling.

git-bob try to implement this

Detailed instructions for bio-image analysis using Python (feel free to modify)

Detailed Python Bio-image Analysis instructions

If the following tasks are requested, we can adapt the code corresponding snippets:

Viewing images using stackview

When you use stackview, you always start by importing the library: import stackview.

• Showing an image stored in variable image and a segmented image stored in variable labels on top with animated blending. Also works with two images or two label images.
  stackview.animate_curtain(image, labels)

• Showing an animation / timelapse image stored in variable image.
  stackview.animate(image)

• Save an animation / timelapse stored in variable image with specified frame delay to a file.
  stackview.animate(image, filename="output.gif", frame_delay_ms=100)

• Display an image stored in a variable image (this also works with label images). Prefer stackview.insight over matplotlib.pyplot.imshow!
  stackview.insight(image)

• Display an image as a label image explicitly.
  stackview.imshow(image, labels=True)

Processing images using the napari-simpleitk-image-processing (nsitk) Python library.

When you use nsitk, you always start by importing the library: import napari_simpleitk_image_processing as nsitk.
When asked for specific tasks, you can adapt one of the following code snippets:

• Apply a median filter to an image to remove noise while preserving edges.
  nsitk.median_filter(image, radius_x=2, radius_y=2)

• Applies Otsu's threshold selection method to an intensity image and returns a binary image (also works with intermodes, kittler_illingworth, li, moments, renyi_entropy, shanbhag, yen, isodata, triangle, huang and maximum_entropy instead of otsu).
  nsitk.threshold_otsu(image)

• Computes the signed Maurer distance map of the input image.
  nsitk.signed_maurer_distance_map(binary_image)

• Detects edges in the image using Canny edge detection.
  nsitk.canny_edge_detection(image, lower_threshold=0, upper_threshold=50)

• Identifies the regional maxima of an image.
  nsitk.regional_maxima(image)

• Rescales the intensity of an input image to a specified range.
  nsitk.rescale_intensity(image, output_minimum=0, output_maximum=255)

• Applies the Sobel operator to an image to find edges.
  nsitk.sobel(image)

• Enhances the contrast of an image using adaptive histogram equalization.
  nsitk.adaptive_histogram_equalization(image, alpha=0.3, beta=0.3, radius_x=5, radius_y=5)

• Applies a standard deviation filter to an image.
  nsitk.standard_deviation_filter(image, radius_x=5, radius_y=5)

• Labels the connected components in a binary image.
  nsitk.connected_component_labeling(binary_image)

• Labels objects in a binary image and can split object that are touching..
  nsitk.touching_objects_labeling(binary_image)

• Applies the Laplacian of Gaussian filter to find edges in an image.
  nsitk.laplacian_of_gaussian_filter(image, sigma=1.0)

• Identifies h-maxima of an image, suppressing maxima smaller than h.
  nsitk.h_maxima(image, height=10)

• Removes background in an image using the Top-Hat filter.
  nsitk.white_top_hat(image, radius_x=5, radius_y=5)

• Computes basic statistics for labeled object regions in an image.
  nsitk.label_statistics(image, label_image, size=True, intensity=True, shape=False)

• Computes a map from a label image where the pixel intensity corresponds to the number of pixels in the given labeled object (analogously work elongation_map, feret_diameter_map, roundness_map).
  nsitk.pixel_count_map(label_image)

Processing images using napari-segment-blobs-and-things-with-membranes (nsbatwm)

If you use this plugin, you need to import it like this: import napari_segment_blobs_and_things_with_membranes as nsbatwm.
You can then use it for various purposes:

• Denoise an image using a Gaussian filter
  nsbatwm.gaussian_blur(image, sigma=1)

• Denoise an image, while preserving edges:
  nsbatwm.median_filter(image, radius=2)

• Denoise an image using a percentile (similar to median, but free in choosing the percentile)
  nsbatwm.percentile_filter(image, percentile=50, radius=2)

• Determine the local minimum intensity for every pixel (also works with maximum)
  nsbatwm.minimum_filter(image, radius=2)

• Enhance edges
  nsbatwm.gaussian_laplace(image, sigma=2)

• Remove background from an image using the Top-Hat filter
  nsbatwm.white_tophat(image, radius=2)

• Remove background from an image using the Rolling-Ball method
  nsbatwm.subtract_background(membranes, rolling_ball_radius=15)

• Uses combination of Voronoi tesselation and Otsu's threshold method for segmenting an image
  nsbatwm.voronoi_otsu_labeling(blobs, spot_sigma=3.5, outline_sigma=1)

• Apply a Gaussian blur, Otsu's threshold for binarization and returns a label image
  nsbatwm.gauss_otsu_labeling(blobs, outline_sigma=1)

• Binarize an image using a threshold determined using Otsu's method (also works with li, triangle, yen, mean methods)
  nsbatwm.threshold_otsu(blobs)

• Split touching objects in a binary image
  nsbatwm.split_touching_objects(binary, sigma=3.5)

• Identify individual objects in a binary image using Connected Component labeling
  nsbatwm.connected_component_labeling(binary)

• Apply a Watershed algorithm to an an image showing membrane-like structures and a label image that serves as seeds for the watershed
  nsbatwm.seeded_watershed(membranes_image, labeled_seeds)

• Apply a Watershed algorithm to an image showing membrane-like structures. The seeds for the watershed are internally determined using local minima.
  nsbatwm.local_minima_seeded_watershed(membrane_image, spot_sigma=10, outline_sigma=0)

• Dilate labels to increase their size
  nsbatwm.expand_labels(label_image, distance=1)

• Smooths outlines of label images by determining the most popular label locally
  nsbatwm.mode_filter(label_image, radius=10)

• Remove labels that touch the image border
  nsbatwm.remove_labels_on_edges(label_image)

• Skeletonize labels
  nsbatwm.skeletonize(labels)

Working with Pandas DataFrames

In case a pandas DataFrame, e.g. df is the result of a code block, just write df.head()
by the end so that the user can see the intermediate result.

Processing images with scikit-image (skimage)

• Load an image file from disc and store it in a variable:
  from skimage.io import imread
  image = imread(filename)

• Save an image file to disc:
  from skimage.io import imwrite
  imread(filename, image)

• Expanding labels by a given radius in a label image works like this:
  from skimage.segmentation import expand_labels
  expanded_labels = expand_labels(label_image, distance=10)

• Turn a label image into an RGB image, e.g. for saving as png:
  from skimage import color
  rgb_image = (color.label2rgb(labels, bg_label=0, kind='overlay')*255).astype('uint8')

• Measure properties of labels with respect to an image works like this:
  import pandas as pd
  from skimage.measure import regionprops_table
  properties = ['label', 'area', 'mean_intensity'] # add more properties if needed
  measurements = regionprops_table(label_image, intensity_image=image, properties=properties)
  df = pd.DataFrame(measurements)

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I created a solution for nuclei segmentation using Voronoi-Otsu-Labeling in a Jupyter notebook. The workflow starts by downloading and saving the input image 'human_mitosis_small.tif', followed by creating a notebook that processes the first channel of the image to segment nuclei, and finally saves the result as a PNG file.

• human_mitosis_small.tif Downloaded and saved the input image for nuclei segmentation.

• nuclei_segmentation.ipynb Created a new notebook implementing Voronoi-Otsu-Labeling for nuclei segmentation.