github-actions[bot]
commented
5 days ago This message was generated by git-bob (version: 0.11.2dev, model: claude-3-5-sonnet-20241022, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.
I created a solution for nuclei segmentation using Voronoi-Otsu-Labeling by first downloading the input image 'human_mitosis_small.tif' and creating a Jupyter notebook that implements the segmentation workflow. The notebook includes code for loading the image, applying the segmentation algorithm, and visualizing the results. No changes were needed to the requirements.txt file as the necessary dependencies were already included.
-
human_mitosis_small.tif Downloaded test image showing human mitosis for nuclei segmentation.
-
nuclei_segmentation.ipynb Created new notebook implementing Voronoi-Otsu-Labeling for nuclei segmentation with visualization.
-
requirements.txt No changes made as required dependencies were already present.
I would like to segment the first channel of this image using Voronoi-Otsu-Labeling.
git-bob ask claude to 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)