Earth Observation path to imaging introduction tutorial #6970
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@@ -11,13 +11,16 @@ questions: | |||||
| - "How do I display images in Galaxy?" | ||||||
| - "How do I filter images in Galaxy?" | ||||||
| - "How do I segment simple images in Galaxy?" | ||||||
| - "Can the same image analysis workflow be reused on data from another discipline, such as Earth observation?" | ||||||
| objectives: | ||||||
| - "How to handle images in Galaxy." | ||||||
| - "How to perform basic image processing in Galaxy." | ||||||
| - "Reuse the same segment-and-count workflow on an Earth observation image (cross-discipline reuse)." | ||||||
| key_points: | ||||||
| - The **Image Info** tool can provide valuable metadata information of an image. | ||||||
| - TIFF files cannot viewed directly in most web browser, so a visualization plugin must be used. | ||||||
| - For visualization, images with a bit-depth more than 8-bit have to be histogram equalized. | ||||||
| - The same threshold–label–count workflow can be applied across scientific domains—here a cell-counting workflow is reused unchanged to count supraglacial melt ponds in a Sentinel-2 satellite image. | ||||||
| time_estimation: "1H" | ||||||
| follow_up_training: | ||||||
| - | ||||||
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@@ -28,17 +31,20 @@ follow_up_training: | |||||
| contributions: | ||||||
| authorship: | ||||||
| - thomaswollmann | ||||||
| - kostrykin | ||||||
| - annefou | ||||||
| - shiltemann | ||||||
| funding: | ||||||
| - elixir-europe | ||||||
| - oscars | ||||||
| tags: | ||||||
| - Image segmentation | ||||||
| - Image thresholding | ||||||
| - Conversion | ||||||
| - Object counting | ||||||
| - Overlay | ||||||
| - Fluorescence microscopy | ||||||
| - Earth observation | ||||||
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| --- | ||||||
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@@ -56,15 +62,19 @@ This tutorial shows how to use Galaxy to perform basic image analysis tasks such | |||||
| > | ||||||
| {: .agenda} | ||||||
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| The same Galaxy image analysis workflow can be applied to images from very different scientific domains. To account for that, this tutorial can be followed using an image either from a **Bioimaging** dataset (cell nuclei, fluorescence microscopy) or from **Earth observation** (supraglacial melt ponds in a Sentinel-2 satellite scene). The analysis steps are identical, the only differences concern the input images and the interpretation of parameters. Choose the data that better suits your needs: | ||||||
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| {% include _includes/cyoa-choices.html option1="Bioimaging" option2="Earth observation" default="Bioimaging" text="Choose the image data to work with. The image data from the **Bioimaging** dataset contains fluorescence microscopy images of DAPI-stained cell nuclei. The **Earth observation** image is a Sentinel-2 water-index image of Greenland supraglacial melt ponds. The analysis workflow is the same for both." %} | ||||||
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| # Getting Data | ||||||
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| <div class="Bioimaging" markdown="1"> | ||||||
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| The bioimaging dataset required for this tutorial is available from [Zenodo]({{ page.zenodo_link }}) and contains a screen of [DAPI](https://en.wikipedia.org/wiki/DAPI) stained [HeLa](https://en.wikipedia.org/wiki/HeLa) nuclei ([more information]({{ page.zenodo_link }})). We will use a sample image from this dataset for training basic image processing skills in Galaxy. | ||||||
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| Our objective is to automatically count the number of cells contained in an image. In order to achieve this, we will enhance the quality of the image, automatically detect and segment the nuclei, and finally count them. | ||||||
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| > <hands-on-title>Bioimaging Data Upload</hands-on-title> | ||||||
| > | ||||||
| > 1. If you are logged in, create a new history for this tutorial | ||||||
| > | ||||||
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@@ -91,6 +101,46 @@ Our objective is to automatically count the number of cells contained in this im | |||||
| > {% snippet faqs/galaxy/datasets_rename.md %} | ||||||
| {: .hands_on} | ||||||
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| </div> | ||||||
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| <div class="Earth-observation" markdown="1"> | ||||||
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| The dataset for the Earth observation path is a single-band 16-bit **water-index image** derived from a Sentinel-2 satellite scene of the Greenland ice sheet, in which supraglacial melt ponds appear as bright objects. | ||||||
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| Our objective is to automatically count the number of melt ponds in the satellite image. In order to achieve this, we will enhance the quality of the image, automatically detect and segment the ponds, and finally count them. | ||||||
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| > <hands-on-title>Earth Observation Data Upload</hands-on-title> | ||||||
| > | ||||||
| > 1. If you are logged in, create a new history for this tutorial | ||||||
| > | ||||||
| > {% snippet faqs/galaxy/histories_create_new.md %} | ||||||
| > | ||||||
| > 2. Import the Sentinel-2 melt-pond water-index image from [Zenodo](https://doi.org/10.5281/zenodo.20960690). | ||||||
| > - **Important:** Choose the type of data as `tiff`. | ||||||
| > | ||||||
| > ``` | ||||||
| > https://zenodo.org/records/20960690/files/melt_ponds_water_index.tif | ||||||
| > ``` | ||||||
| > | ||||||
| > {% snippet faqs/galaxy/datasets_import_via_link.md %} | ||||||
| > | ||||||
| > 3. Rename {% icon galaxy-pencil %} the dataset to `input.tiff` | ||||||
| > | ||||||
| > {% snippet faqs/galaxy/datasets_rename.md %} | ||||||
| {: .hands_on} | ||||||
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| > <comment-title> Counting melt ponds with a cell-counting workflow </comment-title> | ||||||
| > | ||||||
| > In the Earth observation path, we reuse *the same* segment-and-count workflow that was built for counting of stained cell nuclei to count *supraglacial melt ponds* on the Greenland ice sheet. This is a cross-discipline experiment from the [OSCARS-FIESTA](https://oscars-project.eu/projects/fair-image-analysis-across-sciences) project. Why does it work? Melt ponds have a high ice-adapted water index, so in a water-index image they appear as *bright blobs on a darker background*, which is the same characteristic that is exploited for segmenting cell nuclei. The same threshold–label–count steps that count nuclei count melt ponds; only the *index* and the *interpretation of the threshold* are domain-specific. | ||||||
| {: .comment} | ||||||
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| > <comment-title> How the satellite image was prepared </comment-title> | ||||||
| > | ||||||
| > The image is a single-band ice-adapted normalised-difference water index, NDWI<sub>ice</sub> = (blue − red)/(blue + red) (Williamson et al. 2018, [doi:10.5194/tc-12-3045-2018](https://doi.org/10.5194/tc-12-3045-2018)), computed from a Sentinel-2 Level-2A scene (tile T22WEV, 2019-07-23) over the south-west Greenland ablation zone and rescaled to 16-bit so that melt ponds are bright. The preprocessing notebooks, the full Galaxy run provenance, the validation against an independently published lake map, and a citable archive are in the [OSCARS-FIESTA example repository](https://github.com/annefou/fiesta-galaxy-meltponds-eo). | ||||||
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| {: .comment} | ||||||
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| </div> | ||||||
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| # Image Metadata Extraction | ||||||
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@@ -105,12 +155,12 @@ Now, we can extract metadata from an image. | |||||
| > > <question-title></question-title> | ||||||
| > > | ||||||
| > > 1. What is the datatype? | ||||||
| > > 2. What are the pixel dimensions? | ||||||
| > > 3. How many bits per pixel are used? | ||||||
| > > | ||||||
| > > > <solution-title></solution-title> | ||||||
| > > > 1. TIFF | ||||||
| > > > 2. 1344×1024 (bioimaging data) or 1000×1000 (Earth observation data) | ||||||
| > > > 3. 16 | ||||||
| > > {: .solution } | ||||||
| > {: .question} | ||||||
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@@ -130,14 +180,20 @@ Not all tools can handle all image formats. Especially proprietary microscope im | |||||
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| Your image should look something like this: | ||||||
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| <div class="Bioimaging" markdown="1"> | ||||||
| {: width="75%"} | ||||||
| </div> | ||||||
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| <div class="Earth-observation" markdown="1"> | ||||||
| {: width="75%"} | ||||||
| </div> | ||||||
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| > <question-title></question-title> | ||||||
| > | ||||||
| > You can observe that the image content is barely visible. Why? | ||||||
| > | ||||||
| > > <solution-title></solution-title> | ||||||
| > > The image is 16-bit, but its intensity values cover only a small fraction of the range that 16 bits can represent (0 to 65535, where 0 corresponds to black and 65535 corresponds to white). Therefore, for improved visibility the intensity histogram of the image should be normalized first. | ||||||
| > {: .solution } | ||||||
| {: .question} | ||||||
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@@ -155,9 +211,17 @@ Next we will normalize the histogram to improve the contrast. We do this using a | |||||
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| Your image should now look something like this: | ||||||
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| <div class="Bioimaging" markdown="1"> | ||||||
| {: width="75%"} | ||||||
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| We can now clearly make out the stained nuclei. Next we will automatically detect these features and segment the image. | ||||||
| </div> | ||||||
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| <div class="Earth-observation" markdown="1"> | ||||||
| {: width="75%"} | ||||||
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| We can now clearly make out the melt ponds. Next we will automatically detect these features and segment the image. | ||||||
| </div> | ||||||
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| # Image Filtering | ||||||
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@@ -179,12 +243,24 @@ Specific features of interest (e.g., edges, noise) can be enhanced or suppressed | |||||
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| Your image should now look something like this: | ||||||
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| <div class="Bioimaging" markdown="1"> | ||||||
| {: width="75%"} | ||||||
| </div> | ||||||
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| <div class="Earth-observation" markdown="1"> | ||||||
| {: width="75%"} | ||||||
| </div> | ||||||
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| # Segmentation | ||||||
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| <div class="Bioimaging" markdown="1"> | ||||||
| Objects of interest like cell nuclei can be segmented by using a smoothed image and thresholding. Moreover, the results can be overlayed with the original image. | ||||||
| </div> | ||||||
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| <div class="Earth-observation" markdown="1"> | ||||||
| Objects of interest like melt ponds can be segmented by using a smoothed image and thresholding. Moreover, the results can be overlayed with the original image. | ||||||
| </div> | ||||||
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| > <hands-on-title>Segment image</hands-on-title> | ||||||
| > | ||||||
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@@ -211,9 +287,14 @@ Objects of interest like nuclei can be segmented by using a smoothed image and t | |||||
| > > > - {% icon param-file %} *"Input image"*: `input_segmented_labeled` file (output of {% tool [Convert binary image to label map](toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.5+galaxy0) %}) | ||||||
| > > > - *"Histogram equalization algorithm"*: `CLAHE` | ||||||
| > > > | ||||||
| > > > The information contained in the original image has now become visible to the human eye. | ||||||
| > > > | ||||||
| > > > **Bioimaging data:** | ||||||
| > > >  | ||||||
| > > > | ||||||
| > > > **Earth observation data:** | ||||||
| > > >  | ||||||
| > > > | ||||||
| > > {: .solution } | ||||||
| > {: .question} | ||||||
| > | ||||||
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@@ -235,20 +316,29 @@ Objects of interest like nuclei can be segmented by using a smoothed image and t | |||||
| > > How many objects were segmented? | ||||||
| > > | ||||||
| > > > <solution-title></solution-title> | ||||||
| > > > The {% tool [Count objects in label map](toolshed.g2.bx.psu.edu/repos/imgteam/count_objects/ip_count_objects/0.0.5-2) %} tool counted: | ||||||
| > > > - **Bioimaging data:** 425 nuclei. | ||||||
| > > > - **Earth observation data:** 13 melt ponds, that are the major supraglacial lakes. A single Gaussian-smoothing + Otsu pass detects the large lakes; the full workflow in the [example repository](https://github.com/annefou/fiesta-galaxy-meltponds-eo) (median denoising and the literature water-index threshold) resolves the smaller ponds too, about 90 in this scene. | ||||||
| > > {: .solution } | ||||||
| > {: .question} | ||||||
| {: .hands_on} | ||||||
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| The resulting image should look something like this: | ||||||
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| <div class="Bioimaging" markdown="1"> | ||||||
| {: width="75%"} | ||||||
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| We see the segmentation mask overlayed; each detected object (nucleus) is labeled with its ID value. | ||||||
| </div> | ||||||
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| <div class="Earth-observation" markdown="1"> | ||||||
| {: width="75%"} | ||||||
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| We see the detected supraglacial melt ponds outlined (red contours) on the water-index image. | ||||||
| </div> | ||||||
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| # Conclusion | ||||||
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| With the help of just a few simple steps, we were able to detect the locations of the objects of interest, and count them. | ||||||
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| In this exercise you imported images into Galaxy, extracted meta information from an image, learned how to visually inspect images, filtered the image, and segmented and counted objects using Galaxy. You also saw that the same workflow can be used across different scientific disciplines, including bioimaging and Earth observation. | ||||||
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I re-phrased the text above to improve clarity, but I'm not sure myself what the last 1-2 sentences actually mean. Can you please try to phrase them more clearly?