[Analysis Request]: Cell segmentation

[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, the dark zones between the bright membranes. Afterwards, I would like to measure the average area of the cells.

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]

I presume the watershed algorithm does the job. We need background removal as a first processing step and a distance map to identify local minima close by the centers of the cells.

git-bob think about how to do this.

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.3.2, 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 using the watershed algorithm, we need to perform the following steps:

1. Load the image and preprocess it by enhancing the contrast and removing noise.
2. Apply a threshold to create a binary image.
3. Compute the distance transform to identify the background.
4. Use the watershed algorithm to segment the cells.

Here’s a minimal Python code snippet utilizing scikit-image and numpy for this task:

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

# Load the image
image = io.imread('path_to_image/membrane2d.png', as_gray=True)

# Enhance the contrast
image_enhanced = filters.rank.enhance_contrast(image, morphology.disk(3))

# Thresholding
binary = image_enhanced > filters.threshold_otsu(image_enhanced)

# Compute the distance transform
distance = ndi.distance_transform_edt(binary)

# Find local maxima
local_maxi = feature.peak_local_max(distance, indices=False, labels=binary)

# Generate markers
markers = ndi.label(local_maxi)[0]

# Apply watershed
labels = segmentation.watershed(-distance, markers, mask=binary)

# Measure regions
props = measure.regionprops(labels)
areas = [prop.area for prop in props]
average_area = np.mean(areas)

print(f"Average cell area: {average_area:.2f} pixels")

# Plot results
fig, ax = plt.subplots(ncols=3, figsize=(12, 4))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original')
ax[1].imshow(labels, cmap='nipy_spectral')
ax[1].set_title('Watershed Segmentation')
ax[2].imshow(labels, cmap='nipy_spectral')
for prop in props:
    y0, x0 = prop.centroid
    ax[2].text(x0, y0, f'{prop.area:.0f}', color='white', fontsize=8)
ax[2].set_title('Labeled Cells')

for a in ax:
    a.axis('off')

plt.tight_layout()
plt.show()

Replace 'path_to_image/membrane2d.png' with the path to the image file. This script segments the cells and calculates the average cell area.

[haesleinhuepf]

Awesome, please download my image above to "data/membrane2d.png" and take code above to make a new jupyter notebook in the "notebooks" folder. When opening the image from the notebook, make sure the filename is "../data/membrane2d.png".

git-bob implement this!

[haesleinhuepf]

Ok, I would like to change the following things:

• Use white top-hat for background removal step instead of enhance_contrast
• After determining local_maxi , a list of coordinates, we need to create a binary image where pixels at these coordinates are 1 and all other pixels are 0. This image should be passed as markers to the watershed algorithm.
• The distance map passed to the peak_local_max function should be negated.

git-bob think about this

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.3.2, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To implement the requested changes, here's the updated code snippet:

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

### Load the image
image = io.imread('../data/membrane2d.png', as_gray=True)

### Background removal using white top-hat
selem = morphology.disk(15)
image_tophat = morphology.white_tophat(image, selem)

### Thresholding
binary = image_tophat > filters.threshold_otsu(image_tophat)

### Compute the distance transform and negate it
distance = ndi.distance_transform_edt(binary)
distance = -distance

### Find local maxima
local_maxi = feature.peak_local_max(distance, indices=False, labels=binary)

### Generate markers
markers = ndi.label(local_maxi)[0]

### Apply watershed
labels = segmentation.watershed(distance, markers, mask=binary)

### Measure regions
props = measure.regionprops(labels)
areas = [prop.area for prop in props]
average_area = np.mean(areas)

print(f"Average cell area: {average_area:.2f} pixels")

### Plot results
fig, ax = plt.subplots(ncols=3, figsize=(12, 4))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original')
ax[1].imshow(labels, cmap='nipy_spectral')
ax[1].set_title('Watershed Segmentation')
ax[2].imshow(labels, cmap='nipy_spectral')
for prop in props:
    y0, x0 = prop.centroid
    ax[2].text(x0, y0, f'{prop.area:.0f}', color='white', fontsize=8)
ax[2].set_title('Labeled Cells')

for a in ax:
    a.axis('off')

plt.tight_layout()
plt.show()

This code will be added to a new Jupyter notebook in the "notebooks" folder. Please ensure the image path is correctly set to "../data/membrane2d.png".

[haesleinhuepf]

You forgot to implement this point:

• After determining local_maxi , a list of coordinates, we need to create a binary image where pixels at these coordinates are 1 and all other pixels are 0. This image should be passed as markers to the watershed algorithm.

git-bob think about this

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.4.0, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

Here is the corrected version of the implementation. I have addressed the issue of creating a binary image for markers.

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

##### Load the image
image = io.imread('../data/membrane2d.png', as_gray=True)

##### Background removal using white top-hat
selem = morphology.disk(15)
image_tophat = morphology.white_tophat(image, selem)

##### Thresholding
binary = image_tophat > filters.threshold_otsu(image_tophat)

##### Compute the distance transform and negate it
distance = ndi.distance_transform_edt(binary)
distance = -distance

##### Find local maxima
local_maxi = feature.peak_local_max(distance, indices=False, labels=binary)

##### Create binary image for markers
markers = np.zeros_like(binary, dtype=bool)
markers[tuple(zip(*np.transpose(local_maxi.nonzero())))] = True

##### Label markers
markers, _ = ndi.label(markers)

##### Apply watershed
labels = segmentation.watershed(distance, markers, mask=binary)

##### Measure regions
props = measure.regionprops(labels)
areas = [prop.area for prop in props]
average_area = np.mean(areas)

print(f"Average cell area: {average_area:.2f} pixels")

##### Plot results
fig, ax = plt.subplots(ncols=3, figsize=(12, 4))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original')
ax[1].imshow(labels, cmap='nipy_spectral')
ax[1].set_title('Watershed Segmentation')
ax[2].imshow(labels, cmap='nipy_spectral')
for prop in props:
    y0, x0 = prop.centroid
    ax[2].text(x0, y0, f'{prop.area:.0f}', color='white', fontsize=8)
ax[2].set_title('Labeled Cells')

for a in ax:
    a.axis('off')

plt.tight_layout()
plt.show()

This code will be added to a new Jupyter notebook in the "notebooks" folder. Please ensure the image path is correctly set to "../data/membrane2d.png".

[haesleinhuepf]

Ok great, please download my image above to "data/membrane2d.png" and take code above to make a new jupyter notebook in the "notebooks" folder. When opening the image from the notebook, make sure the filename is "../data/membrane2d.png".

git-bob implement this!