Add tutorial: Where to start with bioimage analysis in Galaxy

[dianichj]

Summary

Hi everyone! 👋 This PR adds a tutorial for the GTN Imaging section: “Where to start with bioimage analysis in Galaxy.” It’s intended to be the foundational “compass” tutorial—a bridge for newcomers to move from viewing images as "pictures" to treating them as quantitative data. It complements the existing intro and emphasizes FAIR-by-design principles within Galaxy.

Linked to issue: #36

What’s included

• Conceptual Foundations: Pixels/voxels, 5D hyperstacks (XYZCT), bit-depth, and spatial calibration.
• Galaxy Onboarding: Handling proprietary formats via Bio-Formats, OME-NGFF standards, and the OMERO route.
• Hands-on "First Steps": Practical modules for metadata inspection, filtering, thresholding, and validation.
• Logical Roadmaps: A decision tree to help users choose between classical computer vision (CellProfiler) and AI (Cellpose/StarDist).
• The Pitfall Guardrail: Warnings on JPEG compression, RGB merging, and photobleaching.

Supporting Files

• tutorial.bib: Might need a few more citations or formatting tweaks.
• data-library.yaml: Still needs the example datasets configuration.
• faqs/index.md & workflows/index.md: Basic structures are in place.

Status: Work in Progress 🧪

This is a functional draft, but still needs some love:

• Visuals: Adding missing images and screenshots.
• Hands-on: Final polishing of the step-by-step instructions.
• Editorial: Cleaning up placeholders like "(Add ...)" and smoothing the text.

Review Focus 🔍

I’d love to get your thoughts on:

1. Structure: Does the flow make sense for a total beginner?
2. Next Steps: Is the roadmap clear enough to know where to go after this?
3. Tools: Are we highlighting the best current practices in Galaxy (e.g., Cellpose-SAM)?
4. Clarity: Is any part too "jargon-heavy"?

Checklist

• Tutorial front matter added.
• Finalize data-library.yaml.
• Replace image placeholders with final figures.
• Verify tutorial tags and metadata.

Thanks a lot for your time and feedback! I'm really looking forward to your ideas and collaboration to make this better. 🚀

@beatrizserrano
@kostrykin
@rmassei

[github-actions]

Remaining comments which cannot be posted as a review comment to avoid GitHub Rate Limit

GTN Lint

---

🚫 [GTN Lint] <GTN:009> _{reported by reviewdog 🐶}
This tool identifier looks incorrect, it doesn't have the right number of segments.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 348 in f050a38

| **{% tool [CellProfiler](https://usegalaxy.eu/root?tool_id=interactive_tool_cellprofiler) %}** | High-content screening & automation | Allows you to build a complex multi-step "pipeline" and run it on thousands of images consistently. |

---

🚫 [GTN Lint] <GTN:009> _{reported by reviewdog 🐶}
This tool identifier looks incorrect, it doesn't have the right number of segments.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 381 in f050a38

* **{% tool [QuPath IT](https://usegalaxy.eu/root?tool_id=interactive_tool_qupath) %}:** The gold standard for digital pathology. Use this for large tissue sections and to access **StarDist** segmentation.

---

🚫 [GTN Lint] <GTN:009> _{reported by reviewdog 🐶}
This tool identifier looks incorrect, it doesn't have the right number of segments.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 382 in f050a38

* **{% tool [Ilastik IT](https://usegalaxy.eu/root?tool_id=interactive_tool_ilastik) %}:** Best for "training by example"—manually paint a few cells to teach the computer how to segment the rest based on texture.

---

🚫 [GTN Lint] <GTN:009> _{reported by reviewdog 🐶}
This tool identifier looks incorrect, it doesn't have the right number of segments.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 383 in f050a38

* **{% tool [Cellpose IT](https://usegalaxy.eu/root?tool_id=interactive_tool_cellpose) %}:** & **{% tool [Cellprofiler IT](https://usegalaxy.eu/root?tool_id=interactive_tool_cellprofiler) %}:** Useful for building and fine-tuning your parameters visually before running a massive batch job.

---

🚫 [GTN Lint] <GTN:033> _{reported by reviewdog 🐶}
The icon (param-conditional) could not be found, please add it to _config.yml.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 223 in f050a38

> - {% icon param-conditional %} *"Type of image data to process"*: `2-D image data (or series thereof)`

---

🚫 [GTN Lint] <GTN:033> _{reported by reviewdog 🐶}
The icon (param-conditional) could not be found, please add it to _config.yml.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

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> - {% icon param-conditional %} *"Filter type"*: `Median`

---

🚫 [GTN Lint] <GTN:033> _{reported by reviewdog 🐶}
The icon (param-conditional) could not be found, please add it to _config.yml.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

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> - {% icon param-conditional %} *"Thresholding method"*: `Globally adaptive / Otsu`

---

⚠️ [GTN Lint] <GTN:020> _{reported by reviewdog 🐶}
This looks like a heading, but isn't. Please use proper semantic headings where possible. You should check the heading level of this suggestion, rather than accepting the change as-is.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

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**(To be add: image showing simple workflow 1. Raw image-Pixels, 2. Numerical Grid for intensities, 3. Data extraction, 4. Final Data)**

---

⚠️ [GTN Lint] <GTN:020> _{reported by reviewdog 🐶}
This looks like a heading, but isn't. Please use proper semantic headings where possible. You should check the heading level of this suggestion, rather than accepting the change as-is.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

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**(Add Pete B's Image from his book)**

---

⚠️ [GTN Lint] <GTN:020> _{reported by reviewdog 🐶}
This looks like a heading, but isn't. Please use proper semantic headings where possible. You should check the heading level of this suggestion, rather than accepting the change as-is.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

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**(Add example images of how two images that look the same to the human eye have different data)**

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 152 in f050a38

### Why use the Bio-Formats tool suite?

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 208 in f050a38

### Stage A: Pre-processing (cleaning)

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 339 in f050a38

#### A. Standard tools (single images, high-performance & batch)

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 427 in f050a38

### 1. The "JPG" trap

---

🚫 [GTN Lint] <GTN:046> _{reported by reviewdog 🐶}
Please do not include an # Introduction section, it is unnecessary here, just start directly into your text. The first paragraph that is seen by our infrastructure will automatically be shown in a few places as an abstract.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Lines 1 to 2 in f050a38

	---
	layout: tutorial_hands_on

[github-actions]

Remaining comments which cannot be posted as a review comment to avoid GitHub Rate Limit

GTN Lint

---

⚠️ [GTN Lint] <GTN:020> _{reported by reviewdog 🐶}
This looks like a heading, but isn't. Please use proper semantic headings where possible. You should check the heading level of this suggestion, rather than accepting the change as-is.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 49 in 6128c35

**(To be add: image showing simple workflow 1. Raw image-Pixels, 2. Numerical Grid for intensities, 3. Data extraction, 4. Final Data)**

---

⚠️ [GTN Lint] <GTN:020> _{reported by reviewdog 🐶}
This looks like a heading, but isn't. Please use proper semantic headings where possible. You should check the heading level of this suggestion, rather than accepting the change as-is.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 64 in 6128c35

**(Add Pete B's Image from his book)**

---

⚠️ [GTN Lint] <GTN:020> _{reported by reviewdog 🐶}
This looks like a heading, but isn't. Please use proper semantic headings where possible. You should check the heading level of this suggestion, rather than accepting the change as-is.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 126 in 6128c35

**(Add example images of how two images that look the same to the human eye have different data)**

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 152 in 6128c35

### Why use the Bio-Formats tool suite?

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 208 in 6128c35

### Stage A: Pre-processing (cleaning)

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 339 in 6128c35

#### A. Standard tools (single images, high-performance & batch)

---

🚫 [GTN Lint] <GTN:028> _{reported by reviewdog 🐶}
You have skipped a heading level, please correct this.

Listing of Heading Levels

# Introduction
# 1. Know your data (the “digital anatomy” of an image)
## Pixels and voxels
### Bit depth (the range)
### Spatial calibration (the size)
## The 5 dimensions (5D)
## Bit depth: why it matters for science
# 2. How to get your images into Galaxy
### Why use the Bio-Formats tool suite?
# 3. Before you begin: diagnose your data
# 4. The lifecycle of an analysis pipeline
### Stage A: Pre-processing (cleaning)
### Stage B: Segmentation (Defining objects)
### Stage B.2: The Region of Interest (ROI)
### Stage C: Post-processing (Refining)
### Stage D: Quantification (Extracting numbers)
### Stage E: Validation (The sanity check)
# 4. Finding your workflow: modality and tools
## The decision tree: your logical roadmap
## The Galaxy imaging toolbox
#### A. Standard tools (single images, high-performance & batch)
#### B. Interactive tools (Visual exploration)
### Identifying your modality
### Practice: applying the roadmap
# 6. Common pitfalls to avoid
### 1. The “JPG” trap
### 2. The “merged image” mistake
### 3. Ignoring Saturation
# Conclusion
# 7. Glossary of Bioimage Terms
# 8. Next Steps: Choose your Tutorial

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Line 427 in 6128c35

### 1. The "JPG" trap

---

🚫 [GTN Lint] <GTN:046> _{reported by reviewdog 🐶}
Please do not include an # Introduction section, it is unnecessary here, just start directly into your text. The first paragraph that is seen by our infrastructure will automatically be shown in a few places as an abstract.

training-material/topics/imaging/tutorials/where-to-start-bioimaging-galaxy/tutorial.md

Lines 1 to 2 in 6128c35

	---
	layout: tutorial_hands_on

[kostrykin]

Very cool, @dianichj! 🚀🪐

A few comments for Section 1 inside.

[kostrykin]

Another iteration of Section 1 comments :)

[kostrykin]

Need to be a bit careful here, because you introduce RGB images as 3D, but then you say that 3D data uses voxels. Of course, this is not true for 2D RGB images. Maybe do something like:

Suggested change

	* A 2D grayscale image is a **matrix** (2D array): rows × columns
	* A 2D color image is a **3D array**: rows × columns × channels (RGB)
	* A 3D volume is a **3D array**: X × Y × Z
	* A multi-dimensional hyperstack is a **tensor**: a multi-dimensional array (e.g., X × Y × Z × C × T)
	Every point in that array—the **pixel** (2D) or **voxel** (3D)—is a data point representing the number of photons or the signal intensity detected at that specific coordinate. Understanding your "digital anatomy" means knowing exactly how those numbers were recorded, how they are organized across dimensions, how they are spaced and oriented in 3D space, and what the limitations of the employed imaging technique are (e.g., due to over/undersaturation that leads to clipped intensities).
	* A **2D grayscale image** is a **2D array** (matrix): rows × columns
	* A **2D color image** is a **3D array** (tensor): rows × columns × channels (RGB)
	* A **3D grayscale image (volume)** is a **3D array**: X × Y × Z
	* A **3D color image (volume)** is a **4D array**: X × Y × Z × C
	* A multi-dimensional hyperstack is a **tensor**: a multi-dimensional array (e.g., X × Y × Z × C × T)
	Every point in that array—the **pixel** (2D) or **voxel** (3D)—is a data point representing the number of photons or the signal intensity detected at that specific coordinate. Understanding your "digital anatomy" means knowing exactly how those numbers were recorded, how they are organized across dimensions, how they are spaced and oriented in 3D space, and what the limitations of the employed imaging technique are (e.g., due to over/undersaturation that leads to clipped intensities).

---

But actually, instead of the enumeration, I think it would be clearer if there was a small table like:

Image data	Technical representation	Example
2D grayscale image	2D array (matrix)	rows × columns
2D color image	3D array (tensor)	rows × columns × channels (RGB)
3D grayscale image (volume)	3D array (tensor)	slices × rows × columns
3D color image (volume)	4D array (tensor)	slices × rows × columns × channels (RGB)
…	…	…

…and then mention in a brief sentence that ≥4D images are generally called hyperstacks.

[kostrykin]

Suggested change

	The key difference is not whether artifacts appear, but their **magnitude relative to your data range**. Think of it this way: spreading 256 values across a wider range creates larger "jumps" between adjacent intensity levels than spreading 65,536 values across the same range.
	The key difference is not whether artifacts appear, but their **magnitude in relation to your data range**. Think of it this way: spreading 256 values across a wider range creates larger "holes" between adjacent intensity levels than spreading 65,536 values across the same range.

[kostrykin]

It would be good to also briefly mention 32-bit integers. I think the exact numbers are not that important. The tutorial is going to serve as an introductory text, not as a technical reference sheet, right? Such details can still be researched by the readers on their own, when required. Of course, you can still include those numbers, if you want, but I think you don't need to.

[kostrykin]

Some cosmetics: {% icon level %} looks like 🎓

Seems to be kind of a good practice to use these when linking to other tutorials: https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_alevin/tutorial.html#warning-for-the-bench-scientists-and-biologists

Full list of GTN icons is available here: https://training.galaxyproject.org/training-material/topics/contributing/tutorials/create-new-tutorial-content/tutorial.html#icons

Suggested change

	* **The Basics:** [Introduction to Image Analysis]({% link topics/imaging/tutorials/imaging-introduction/tutorial.md %}) – A deeper dive into the fundamental concepts of digital images.
	* **Fluorescence & Screening:** [HeLa Cell Screen Analysis]({% link topics/imaging/tutorials/hela-screen-analysis/tutorial.md %}) – Learn to process high-throughput screens using classical techniques.
	* **Time-Lapse & Events:** [Detection of MitoFlashes]({% link topics/imaging/tutorials/detection-of-mitoflashes/tutorial.md %}) – Track transient biological events over time.
	* **Classical Segmentation:** [Voronoi-based Segmentation]({% link topics/imaging/tutorials/voronoi-segmentation/tutorial.md %}) – A powerful approach for partitioning cells when they are touching but have clear centers.
	* **Advanced AI:** [Imaging using Bioimage Zoo Models]({% link topics/imaging/tutorials/process-image-bioimageio/tutorial.md %}) – Use pre-trained deep learning models for complex segmentation tasks.
	* **Automated Pipelines:** [CellProfiler in Galaxy]({% link topics/imaging/tutorials/tutorial-CP/tutorial.md %}) – Master the use of CellProfiler modules to build end-to-end automated workflows.
	* **Tracking:** [Object Tracking using CellProfiler]({% link topics/imaging/tutorials/object-tracking-using-cell-profiler/tutorial.md %}) – Move from static images to following individual objects through time and space.
	* **The Basics:** {% icon level %} [Introduction to Image Analysis]({% link topics/imaging/tutorials/imaging-introduction/tutorial.md %}) – A deeper dive into the fundamental concepts of digital images.
	* **Fluorescence & Screening:** {% icon level %} [HeLa Cell Screen Analysis]({% link topics/imaging/tutorials/hela-screen-analysis/tutorial.md %}) – Learn to process high-throughput screens using classical techniques.
	* **Time-Lapse & Events:** {% icon level %} [Detection of MitoFlashes]({% link topics/imaging/tutorials/detection-of-mitoflashes/tutorial.md %}) – Track transient biological events over time.
	* **Classical Segmentation:** {% icon level %} [Voronoi-based Segmentation]({% link topics/imaging/tutorials/voronoi-segmentation/tutorial.md %}) – A powerful approach for partitioning cells when they are touching but have clear centers.
	* **Advanced AI:** {% icon level %} [Imaging using Bioimage Zoo Models]({% link topics/imaging/tutorials/process-image-bioimageio/tutorial.md %}) – Use pre-trained deep learning models for complex segmentation tasks.
	* **Automated Pipelines:** {% icon level %} [CellProfiler in Galaxy]({% link topics/imaging/tutorials/tutorial-CP/tutorial.md %}) – Master the use of CellProfiler modules to build end-to-end automated workflows.
	* **Tracking:** {% icon level %} [Object Tracking using CellProfiler]({% link topics/imaging/tutorials/object-tracking-using-cell-profiler/tutorial.md %}) – Move from static images to following individual objects through time and space.

[maartenpaul]

I would also mention ome.tiff here as an open file format including metadata, and if you cite Moore et al. here, it would be good to mention OME-zarr already here, altough it is explained in more detail below.

[maartenpaul]

Maybe reference the tutorial on OMERO here?

[maartenpaul]

I would add that here one would also need to think what you need to quantify.

Then, define your quantitative goals:

 - **What do you want to measure?** (Cell count? Nuclear size? Protein intensity? Colocalization? Movement over time?)
 - **What level of detail do you need?** (Measurements per object, summary statistics across many objects, or maps of where things are located?)
 - **What comparison will you make?** (Differences between conditions, changes across time, or descriptive characterization?)

[maartenpaul]

"you can draw ROIs" ; I would say Qupath often also uses automatic ROI creation

[maartenpaul]

@dianichj Very nice tutorial!
@kostrykin asked me to help him to review the second part of the tutorial. I have added some comments.

[dianichj]

@dianichj Very nice tutorial! @kostrykin asked me to help him to review the second part of the tutorial. I have added some comments.

Thanks a lot @maartenpaul ! Very useful comments, I will try to address them all today.