[Analysis Request]: Segmenting cells

[haesleinhuepf]

Bio-Image Analysis Support Request

Image Upload

📎 Drag & drop your microscopy image here (JPG, PNG, GIF, 512x512 or 1024x1024, 2D only).

Analysis Goal

• What do you want to analyze (e.g., count cells, measure structures, segment regions)?

I would like to segment the cells in this image and measure their area.

Python Tools

• Are you using any specific Python libraries for the analysis? If not, we'll may use:
  • numpy
  • scikit-image
  • stackview
  • pandas

Note: Your images and the text you enter here may be sent to OpenAI's online serivce where we use a large language model to answer your request. Do not upload any data you cannot share openly. Also do not enter any private or secret information. By submitting this github issue, you confirm that you understand these conditions.

Once submitted, @haesleinhuepf will review and consult git-bob, an AI-assistant for bio-image analysis suggestions.

[haesleinhuepf]

git-bob think about this

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.3.1, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To segment the cells in the provided 2D microscopy image and measure their areas, I suggest using the scikit-image library to perform image segmentation. Here is a minimal Python code snippet that utilizes adaptive thresholding and labeling to achieve this task:

from skimage import io, filters, measure, morphology
import matplotlib.pyplot as plt

# Load the image (replace with the correct path or URL)
image = io.imread('image_url_here')

# Convert the image to grayscale if necessary
if len(image.shape) == 3:
    image_gray = rgb2gray(image)
else:
    image_gray = image

# Apply adaptive thresholding
threshold = filters.threshold_otsu(image_gray)
binary_image = image_gray > threshold

# Remove small objects and noise
cleaned_image = morphology.remove_small_objects(binary_image, min_size=150)

# Label connected regions
labeled_image, num_features = measure.label(cleaned_image, return_num=True)

# Measure properties of labeled regions
properties = measure.regionprops_table(labeled_image, properties=['label', 'area'])

# Display results
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[1].imshow(labeled_image, cmap='nipy_spectral')
ax[1].set_title('Segmented Cells')

plt.show()

# Print area of each segmented cell
import pandas as pd
df = pd.DataFrame(properties)
print(df)

Replace 'image_url_here' with the actual path or URL to the image file. This code will output the segmented image and a table of cell areas in pixels. Adjust parameters like min_size in remove_small_objects based on your specific needs.

[haesleinhuepf]

As image_url_here use "membranes.png" and download the image I provided and put it in the data folder of the repository.

Also modify the image analysis workflow so that it makes use of the watershed algorithm instead of Otsu-thresholding.

git-bob think about this

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.3.1, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To address your request to modify the workflow using the watershed algorithm instead of Otsu-thresholding, here's an updated Python code snippet:

from skimage import io, filters, measure, morphology, segmentation, color
import matplotlib.pyplot as plt
from scipy import ndimage as ndi

# Load the image (update the path as necessary)
image = io.imread('data/membranes.png')

# Convert the image to grayscale if necessary
if len(image.shape) == 3:
    image_gray = rgb2gray(image)
else:
    image_gray = image

# Compute the local gradients of the image
gradient = filters.sobel(image_gray)

# Generate markers for the watershed algorithm
markers = ndi.label(image_gray < filters.threshold_otsu(image_gray))[0]

# Apply the watershed algorithm
labeled_image = segmentation.watershed(gradient, markers)

# Remove small objects
cleaned_image = morphology.remove_small_objects(labeled_image, min_size=150)

# Measure properties of labeled regions
properties = measure.regionprops_table(cleaned_image, properties=['label', 'area'])

# Display results
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[1].imshow(color.label2rgb(cleaned_image, image=image, bg_label=0), cmap='nipy_spectral')
ax[1].set_title('Segmented Cells')

plt.show()

# Print area of each segmented cell
import pandas as pd
df = pd.DataFrame(properties)
print(df)

This code replaces the Otsu thresholding with the watershed segmentation based on image gradients and labeled markers. Ensure that the image "membranes.png" is in the data folder as specified.

[haesleinhuepf]

Ok, let's try this solution out. git-bob implement this as a jupyter notebook in the folder "notebooks".

[haesleinhuepf]

This didn't work because the image file location is supposed to be "../data/membranes.png" instead of "data/membranes.png" from the perspective of the notebook.

git-bob implement this again with the correct path.

[haesleinhuepf]

git-bob implement this again with the correct path.

[haesleinhuepf]

The segmentation alreadly looks quite good! However, I think we should apply a background-subtraction algorithm before the segmentation. E.g. a white top-hat with a radius of 10 may do the job.

git-bob think about this!

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.3.1, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To incorporate a background subtraction using a white top-hat filter with a radius of 10 before segmentation, here's an updated version of the workflow as a Jupyter notebook script:

from skimage import io, filters, measure, morphology, segmentation, color
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
import numpy as np
import pandas as pd

### Load the image (update the path correctly)
image = io.imread('../data/membranes.png')

### Convert the image to grayscale if necessary
if len(image.shape) == 3:
    image_gray = rgb2gray(image)
else:
    image_gray = image

### Apply white top-hat transformation for background subtraction
selem = morphology.disk(10)
image_tophat = morphology.white_tophat(image_gray, selem)

### Compute the local gradients of the image after background subtraction
gradient = filters.sobel(image_tophat)

### Generate markers for the watershed algorithm
markers = ndi.label(image_tophat < filters.threshold_otsu(image_tophat))[0]

### Apply the watershed algorithm
labeled_image = segmentation.watershed(gradient, markers)

### Remove small objects
cleaned_image = morphology.remove_small_objects(labeled_image, min_size=150)

### Measure properties of labeled regions
properties = measure.regionprops_table(cleaned_image, properties=['label', 'area'])

### Display results
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[1].imshow(image_tophat, cmap='gray')
ax[1].set_title('Background Subtracted')
ax[2].imshow(color.label2rgb(cleaned_image, image=image, bg_label=0), cmap='nipy_spectral')
ax[2].set_title('Segmented Cells')

plt.show()

### Print area of each segmented cell
df = pd.DataFrame(properties)
print(df)

Make sure to save this code in a Jupyter notebook file located in the "notebooks" folder.

[haesleinhuepf]

Looks good! git-bob implement this.

[haesleinhuepf]

Ok, the segmentation does still not look very good. How could we increase the number of found markers / seeds?

git-bob comment

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.3.1, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To increase the number of found markers or seeds, we can enhance the marker generation step by applying a combination of morphological operations and intensity thresholding. Here’s an updated approach to improve marker detection, which can be incorporated into the existing notebook:

from skimage import io, filters, measure, morphology, segmentation, color, feature
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
import numpy as np
import pandas as pd

##### Load the image (update the path correctly)
image = io.imread('../data/membranes.png')

##### Convert the image to grayscale if necessary
if len(image.shape) == 3:
    image_gray = rgb2gray(image)
else:
    image_gray = image

##### Apply white top-hat transformation for background subtraction
selem = morphology.disk(10)
image_tophat = morphology.white_tophat(image_gray, selem)

##### Compute the local gradients of the image after background subtraction
gradient = filters.sobel(image_tophat)

##### Enhance marker generation
distance = ndi.distance_transform_edt(image_tophat)
local_maxi = feature.peak_local_max(distance, indices=False, min_distance=5)
markers = ndi.label(local_maxi)[0]

##### Apply the watershed algorithm
labeled_image = segmentation.watershed(gradient, markers)

##### Remove small objects
cleaned_image = morphology.remove_small_objects(labeled_image, min_size=150)

##### Measure properties of labeled regions
properties = measure.regionprops_table(cleaned_image, properties=['label', 'area'])

##### Display results
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[1].imshow(image_tophat, cmap='gray')
ax[1].set_title('Background Subtracted')
ax[2].imshow(color.label2rgb(cleaned_image, image=image, bg_label=0), cmap='nipy_spectral')
ax[2].set_title('Segmented Cells')

plt.show()

##### Print area of each segmented cell
df = pd.DataFrame(properties)
print(df)

This code enhances the marker generation by using a distance transform and detecting local maxima to generate more seeds, which should improve segmentation accuracy.

[haesleinhuepf]

Cool! I'd like to see this in action. git-bob implement this

[haesleinhuepf]

git-bob implement this another time. Not sure what went wrong.

[haesleinhuepf]

There seems to be an issue with notebook execution, and with seed finding. Starting from scratch.