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Merge pull request #4317 from kostrykin/main
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updates `imaging-introduction` to newest tool versions
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@bgruening
bgruening authored Aug 14, 2023
2 parents 10b6c38 + 7494ec2 commit 30d5d1d
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64 changes: 33 additions & 31 deletions topics/imaging/tutorials/imaging-introduction/tutorial.md
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Expand Up @@ -74,7 +74,7 @@ Our objective is to automatically count the number of cells contained in this im
>
> {% snippet faqs/galaxy/datasets_import_from_data_library.md %}
>
> 3. **Unzip file** {% icon tool %} with the following parameters:
> 3. {% tool [Unzip](toolshed.g2.bx.psu.edu/repos/imgteam/unzip/unzip/6.0+galaxy0) %} with the following parameters:
> - {% icon param-file %} *"input_file"*: `Zipped ` input file
> - *"Extract single file"*: `Single file`
> - *"Filepath"*: `B2--W00026--P00001--Z00000--T00000--dapi.tif`
Expand All @@ -91,7 +91,7 @@ Now, we can extract metadata from an image.

> <hands-on-title>Extract Image Metadata</hands-on-title>
>
> 1. **Image Info** {% icon tool %} with the following parameters to extract metadata from the image:
> 1. {% tool [Image Info](toolshed.g2.bx.psu.edu/repos/imgteam/image_info/ip_imageinfo/0.2) %} with the following parameters to extract metadata from the image:
> - {% icon param-file %} *"Input Image"*: `input.tif` file (output of the previous step)
> 2. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content and search for image acquisition information
>
Expand All @@ -115,7 +115,7 @@ Not all tools can handle all image formats. Especially proprietary microscope im

> <hands-on-title>Convert Image</hands-on-title>
>
> 1. **Convert image** {% icon tool %} with the following parameters to convert the image to PNG:
> 1. {% tool [Convert image](toolshed.g2.bx.psu.edu/repos/imgteam/bfconvert/ip_convertimage/6.7.0+galaxy0) %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input.tif` file
> - *"Output data type"*: `PNG`
> - *"Pyramid image"*: `No Pyramid`
Expand Down Expand Up @@ -144,12 +144,12 @@ Next we will normalize the histogram to improve the contrast. We do this using a

> <hands-on-title>Normalize Histogram and Convert Image</hands-on-title>
>
> 1. **Histogram equalization** {% icon tool %} with the following parameters to normalize the histogram of the image:
> 1. {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %} with the following parameters to normalize the histogram of the image:
> - {% icon param-file %} *"Source file"*: `input.tif` file
> - *"Histogram Equalization Algorithm"*: `CLAHE`
> 2. Rename {% icon galaxy-pencil %} the generated file to `input_normalized`
> 3. **Convert image** {% icon tool %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input_normalized` file (output of **Histogram equalization** {% icon tool %})
> 3. {% tool [Convert image](toolshed.g2.bx.psu.edu/repos/imgteam/bfconvert/ip_convertimage/6.7.0+galaxy0) %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input_normalized` file (output of {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %})
> - *"Output data type"*: `PNG`
> 4. Rename {% icon galaxy-pencil %} the generated file to `viz_normalized`
> 5. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content
Expand All @@ -167,17 +167,17 @@ Specific features of interest (e.g., edges, noise) can be enhanced or suppressed

> <hands-on-title>Filter image</hands-on-title>
>
> 1. **Filter Image** {% icon tool %} with the following parameters to smooth the image:
> 1. {% tool [Filter Image](toolshed.g2.bx.psu.edu/repos/imgteam/2d_simple_filter/ip_filter_standard/0.0.3) %} with the following parameters to smooth the image:
> - *"Image type"*: `Gaussian Blur`
> - *"Radius/Sigma"*: `3`
> - {% icon param-file %} *"Source file"*: `input.tif` file
> 2. Rename {% icon galaxy-pencil %} the generated file to `input_smoothed`
> 3. **Histogram equalization** {% icon tool %} with the following parameters to normalize the histogram of the image:
> - {% icon param-file %} *"Source file"*: `input_smoothed` file (output of **Filter image** {% icon tool %})
> 3. {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %} with the following parameters to normalize the histogram of the image:
> - {% icon param-file %} *"Source file"*: `input_smoothed` file (output of {% tool [Filter Image](toolshed.g2.bx.psu.edu/repos/imgteam/2d_simple_filter/ip_filter_standard/0.0.3) %})
> - *"Histogram Equalization Algorithm"*: `CLAHE`
> 4. Rename {% icon galaxy-pencil %} the generated file to `input_smoothed_normalized`
> 5. **Convert image** {% icon tool %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input_smoothed_normalized` file (output of **Histogram equalization** {% icon tool %})
> 5. {% tool [Convert image](toolshed.g2.bx.psu.edu/repos/imgteam/bfconvert/ip_convertimage/6.7.0+galaxy0) %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input_smoothed_normalized` file (output of {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %})
> - *"Output data type"*: `PNG`
> 6. Rename {% icon galaxy-pencil %} the generated file to `viz_smoothed_normalized`
> 7. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content and compare the result with `viz_normalized`. You can observe that `viz_smoothed_normalized` has significant reduced noise.
Expand All @@ -194,41 +194,41 @@ Objects of interest like nuclei can be segmented by using a smoothed image and t

> <hands-on-title>Segment image</hands-on-title>
>
> 1. **Auto Threshold** {% icon tool %} with the following parameters to segment the image:
> - {% icon param-file %} *"Source file"*: `input_smoothed` file (output of **Filter image** {% icon tool %})
> 1. {% tool [Auto Threshold](toolshed.g2.bx.psu.edu/repos/imgteam/2d_auto_threshold/ip_threshold/0.0.5) %} with the following parameters to segment the image:
> - {% icon param-file %} *"Source file"*: `input_smoothed` file (output of {% tool [Filter Image](toolshed.g2.bx.psu.edu/repos/imgteam/2d_simple_filter/ip_filter_standard/0.0.3) %})
> - *"Threshold Algorithm"*: `Otsu`
> - *"Dark Background"*: `Yes`
> 2. Rename {% icon galaxy-pencil %} the generated file to `input_segmented`
> 3. **Binary 2 Label** {% icon tool %} with the following parameters to segment the image:
> - {% icon param-file %} *"Binary Image File"*: `input_segmented` file (output of **Auto Threshold** {% icon tool %})
> 3. {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %} with the following parameters to segment the image:
> - {% icon param-file %} *"Binary Image File"*: `input_segmented` file (output of {% tool [Auto Threshold](toolshed.g2.bx.psu.edu/repos/imgteam/2d_auto_threshold/ip_threshold/0.0.5) %})
> 4. Rename {% icon galaxy-pencil %} the generated file to `input_segmented_labeled`
> 5. **Convert image** {% icon tool %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input_segmented_labeled` file (output of **Binary 2 Label** {% icon tool %})
> 5. {% tool [Convert image](toolshed.g2.bx.psu.edu/repos/imgteam/bfconvert/ip_convertimage/6.7.0+galaxy0) %} with the following parameters to convert the image to PNG:
> - {% icon param-file %} *"Input Image"*: `input_segmented_labeled` file (output of {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %})
> - *"Output data type"*: `PNG`
> 6. Rename {% icon galaxy-pencil %} the converted image to `viz_segmented`
>
> > <question-title></question-title>
> >
> > 1. What does **Binary 2 Label** {% icon tool %} do? (Hint: check the tool help section)
> > 1. What does {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %} do? (Hint: check the tool help section)
> > 2. View the `viz_segmented` image from the last step, what do you see?
> > - Can you explain this result?
> > 3. Exercise: Try to make the information in this image better visible (Hint: **Histogram Equalization** {% icon tool %})
> > 3. Exercise: Try to make the information in this image better visible (Hint: {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %})
> >
> > > <solution-title></solution-title>
> > > 1. The tool assigns each connected component (e.g., segmented cell) in the image an object id and stores it as the intensity value.
> > > 2. The image looks completely black.
> > > The object IDs generated by **Binary 2 Label** {% icon tool %} are relatively low.
> > > The object IDs generated by {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %} are relatively low.
> > > Since the IDs are stored as intensity values, these are too low to be visible in this case. Nevertheless, there is more
> > > information in this image than meets the eye.
> > > 3. To make labeled objects visible, the values have to be stretched to a larger range of visible intensity values. We
> > > can do that by equalizing the histogram again:
> > >
> > > - **Histogram equalization** {% icon tool %} with the following parameters to normalize the intensity values:
> > > - {% icon param-file %} *"Source file"*: `input_segmented_labeled` file (output of **Binary 2 Label** {% icon tool %})
> > > - {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %} with the following parameters to normalize the intensity values:
> > > - {% icon param-file %} *"Source file"*: `input_segmented_labeled` file (output of {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %})
> > > - *"Histogram Equalization Algorithm"*: `CLAHE`
> > >
> > > - **Convert image** {% icon tool %} with the following parameters to convert the image to PNG:
> > > - {% icon param-file %} *"Input Image"*: output of **Histogram Equalization** {% icon tool %}
> > > - {% tool [Convert image](toolshed.g2.bx.psu.edu/repos/imgteam/bfconvert/ip_convertimage/6.7.0+galaxy0) %} with the following parameters to convert the image to PNG:
> > > - {% icon param-file %} *"Input Image"*: output of {% tool [Histogram equalization](toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.0.1) %}
> > > - *"Output data type"*: `PNG`
> > >
> > > The information contained in the original image has now become visible to the human eye:
Expand All @@ -238,22 +238,24 @@ Objects of interest like nuclei can be segmented by using a smoothed image and t
> {: .question}
>
>
> 7. **Overlay Images** {% icon tool %} with the following parameters to convert the image to PNG:
> 7. {% tool [Overlay Images](toolshed.g2.bx.psu.edu/repos/imgteam/overlay_images/ip_overlay_images/0.0.3) %} with the following parameters to convert the image to PNG:
> - *"How to visualize the overlay?"*: `Segmentation mask over image`
> - {% icon param-file %} *"Image"*: `viz_normalized` file
> - {% icon param-file %} *"Label image"*: `input_segmented` file (output of **Auto Threshold** {% icon tool %})
> - *"Contour thickness"*: `0.3`
> - {% icon param-file %} *"Label image"*: `input_segmented_labeled` file (output of {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %})
> - *"Contour thickness"*: `2`
> - *"Contour color"*: `red`
> - *"Show labels"*: `yes`
> - *"Label color"*: `yellow`
> 8. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content and assess the segmentation performance
> 9. **Count Objects** {% icon tool %} with the following parameters to count the segmented objects in the image:
> - {% icon param-file %} *"Source file"*: `input_segmented_labeled` file (output of **Binary 2 Label** {% icon tool %})
> 9. {% tool [Count Objects](toolshed.g2.bx.psu.edu/repos/imgteam/count_objects/ip_count_objects/0.0.5) %} with the following parameters to count the segmented objects in the image:
> - {% icon param-file %} *"Source file"*: `input_segmented_labeled` file (output of {% tool [Binary 2 Label](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.4) %})
>
> > <question-title></question-title>
> >
> > How many objects were segmented?
> >
> > > <solution-title></solution-title>
> > > The **Count Objects** {% icon tool %} tool counted 425 objects.
> > > The {% tool [Count Objects](toolshed.g2.bx.psu.edu/repos/imgteam/count_objects/ip_count_objects/0.0.5) %} tool counted 425 objects.
> > {: .solution }
> {: .question}
{: .hands_on}
Expand All @@ -262,7 +264,7 @@ The resulting image should look something like this:

![segmentation mask output image](../../images/imaging-introduction/viz_segmentation_mask.png){: width="75%"}

We see the segmentation mask overlayed.
We see the segmentation mask overlayed; each detected object (nucleus) is labeled with its ID value.

We see that with the help of just a few simple steps, we were able to detect the locations of the stained nuclei, and count them.

Expand Down

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