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Adding introduction episode
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‎config.yaml‎

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# lc: Library Carpentry
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# cp: Carpentries (to use for instructor training for instance)
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# incubator: The Carpentries Incubator
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# Note that you can also use a custom carpentry type. For more info,
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# see the documentation: https://carpentries.github.io/sandpaper-docs/editing.html
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carpentry: 'incubator'
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# Custom carpentry description
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# This will be used as the alt text for the logo
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# carpentry_description: "Custom Carpentry"
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# Overall title for pages.
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title: 'Microscopy Image Analysis with Python and Napari'
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- 03-thresholding-and-segmentation.md
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- 04-measurements.md
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- 05-napari.md
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- introduction.md
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# Information for Learners
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learners:

‎episodes/introduction.md‎

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---
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title: 'Introduction'
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teaching: 10
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exercises: 2
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---
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:::::::::::::::::::::::::::::::::::::: questions
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- What will I learn in this course, and how is it structured?
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- Why use Python for microscopy image analysis?
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- What are digital microscopy images, and how do they differ from everyday images?
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- What kinds of things can I measure from images using Python?
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- How is the course organised and what software and data will I need to follow along?
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::::::::::::::::::::::::::::::::::::::::::::::::
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::::::::::::::::::::::::::::::::::::: objectives
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- Describe the aims and structure of the course
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- Identify the main Python tools used in microscopy image analysis
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- Recognise different types of microscopy image data and formats
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- Verify that the required software and packages are installed
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- Navigate the lesson materials and setup environment
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::::::::::::::::::::::::::::::::::::::::::::::::
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## Welcome
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This course introduces essential tools and techniques for working with digital microscopy images using Python. It is aimed at researchers and students with some experience in microscopy and a basic familiarity with Python. You do not need to be an expert programmer to benefit.
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We’ll use real image data and widely-used open-source Python libraries, and we’ll explore what makes digital microscopy unique—multi-dimensional data, metadata, and challenges like segmentation and feature measurement.
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## What will we do in this course?
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By the end of the course, you will be able to:
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- Open and inspect images in Python
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- Explore and process multi-dimensional datasets (e.g. time series, z-stacks, multi-channel images)
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- Apply filters and perform background correction
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- Segment and measure biological objects (e.g. nuclei, cells)
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- Use tools like Napari for visualising and interacting with image data
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Microscopy image analysis allows you to extract measurable information from biological samples. Examples include:
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- Object size, shape, and area
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- Intensity or signal distribution per region
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- Cell counts, distances, and object relationships
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- Colocalisation of signals across channels
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A typical image analysis workflow looks like:
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1. **Preprocessing** (e.g. background subtraction, filtering)
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2. **Segmentation** (defining objects of interest, such as nuclei)
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3. **Feature extraction** (e.g. area, intensity, shape, location)
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4. **Analysis and interpretation** (e.g. plotting, classification, statistics)
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## Key software tools
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In this course, we’ll use:
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- **Jupyter notebooks**: for writing and running Python interactively
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- **NumPy**: for handling numerical arrays (which is what images are!)
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- **scikit-image**: for general image processing operations
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- **matplotlib**: for plotting and displaying images
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- **Napari**: for interactive viewing and annotation of multi-dimensional images
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All episodes use open-source tools that you can install locally or access via your institution’s JupyterHub (e.g. Noteable for University of Edinburgh users).
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::::::::::::::::::::::::::::::::::::: callout
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### 💡 Reflection
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Have you used any of the following before?
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- Jupyter notebooks
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- Napari
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- Python image libraries (e.g. scikit-image, OpenCV)
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Take a minute to note down which tools you're already familiar with and what you'd like to learn.
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::::::::::::::::::::::::::::::::::::::::::::::::
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## What kind of data?
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We’ll work with real microscopy images, including:
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- Multi-channel fluorescence `.tif` files
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- 3D z-stacks and time series
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- Colour (RGB) images
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- Proprietary formats (e.g. `.nd2`, `.czi`)
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We'll also cover how to read metadata, understand bit depth, and interpret image dimensions.
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::::::::::::::::::::::::::::::::::::: callout
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### Note: RGB vs Scientific Multichannel Images
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Images like `.jpg` or `.png` use RGB colour, where each pixel contains red, green, and blue values.
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In scientific microscopy, multichannel images are often stored as **separate grayscale channels** (e.g. DAPI, GFP, RFP). These can be visualised as colour composites but are fundamentally different from RGB images used in photography.
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:::::::::::::::::::::::::::::::::::::::::::::::::
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## Course structure
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This course is made up of the following episodes:
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1. **Introduction** (this episode)
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2. **Opening and checking an image**
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3. **Exploring image dimensions and channels**
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4. **Basic image processing and filtering**
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5. **Segmentation and object detection**
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6. **Working interactively with Napari**
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7. **Measuring and exporting results**
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Each episode includes code-along demonstrations, exercises, and challenges. The final episode will tie everything together in a small analysis pipeline.
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## Meet the dataset
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Here is one of the images we’ll be working with:
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![Sample fluorescent image](fig/FluorescentCells_3channel_thumb.jpg){alt="Thumbnail of test image"}
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This is a multi-channel fluorescent image showing nuclei, membranes, and cytoplasm in different colours.
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You’ll also work with z-stacks, RGB images, and proprietary formats like `.nd2`.
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➡️ See the [Reference page](../reference) for a quick recap of Python syntax and digital image basics used in this course.
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---
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::::::::::::::::::::::::::::::::::::: keypoints
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- This course is for researchers with some Python and microscopy experience
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- We will use open-source tools for exploring, processing, and analysing images
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- You will learn how to work with multi-dimensional bioimages and extract useful measurements
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- All exercises use real microscopy datasets
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