Medical Image Processing pilot 1 day

Netherlands eScience Center

September 18th, 2024

9:30 - 17:00 CEST

Instructors: Candace Moore, Giulia Crocioni

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General Information

The eScience Center offers a range of workshops and training courses, aimed at PhD candidates and other researchers or research software engineers. We organize workshops covering digital skills needed to put reproducible research into practice. These include online collaboration, reproducible code and good programming practices. We also offer more advanced workshops such as GPU Programming, Parallel Programming, Image Processing and Deep Learning.

Medical image analysis has become an essential tool in clinical research, enabling scientists to extract valuable insights from complex imaging data. This course is designed to enhance your computational skills in medical imaging, leveraging the power of Python to address advanced research questions. You already possess foundational skills in Python, and basic image processing, and this course will build on that knowledge to help you conduct sophisticated analyses with medical images.

The course begins with an overview of medical imaging and progresses to practical techniques for image processing using libraries like scikit-image, pydicom, and SimpleITK. You will then dive into feature extraction, machine learning applications, and statistical methods for analyzing anatomical differences. By the end of the course, you will have a comprehensive understanding of how to apply computational techniques to medical imaging research, enhancing your ability to conduct impactful studies and contribute to the advancement of clinical knowledge.

Who: 

The participant should:

  • be familiar with basic Python: control flow, functions, NumPy
  • understand medical imaging concepts and have experience with image analysis techniques

Recommended:

  • familiarity with the libraries like scikit-image

Where: Science Park 402, 1098 XH Amsterdam. Get directions with OpenStreetMap or Google Maps.

When: September 18th, 2024, 9:30 - 17:00 CEST.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

  • The room is wheelchair / scooter accessible.
  • Accessible restrooms are available.

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Workshop files: You will find all slides, notebooks, archived collaborative documents, and other relevant files in the files folder of the workshop website repository after the workshop.

Contact: Please email or training@esciencecenter.nl for more information.

Code of Conduct

Participants are expected to follow these guidelines:

  • Use welcoming and inclusive language
  • Be respectful of different viewpoints and experiences
  • Gracefully accept constructive criticism
  • Focus on what is best for the community
  • Show courtesy and respect towards other community members

Syllabus

  • Medical Imaging Modalities
  • Working with MRI
  • Registration and Segmentation with SITK
  • Preparing Images for Machine Learning
  • Anonymizing Medical Images
  • Generative AI in Medical Imaging

Schedule

Day 1

09:30 Welcome and icebreaker
09:45 Course and Center Introduction
10:00 Introduction to Medical Imaging
10:30 MRI I
11:00 Coffee Break
11:15 MRI II
12:30 Lunch
13:30 Registration and Segmentation with SITK
15:00 Coffee Break
15:15 Preparing Images for ML
16:00 Anonmymization
16:30 Generative AI
16:45 Wrap-up
17:00 Drinks

Setup

To participate in this workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

Software setup

Setup

Installing the Python Environment

We’ll use Mamba (a faster alternative to Conda fully compatible with it) to set up the Python environment. The environment YML file for the lesson is available on GitHub.

Installing Mamba via Miniforge

Miniforge is a minimal installer for Conda that includes Mamba. Here are the installation instructions for different operating systems:

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Windows

  1. Download the Miniforge3 installer for Windows from the official GitHub repository.
  2. Run the installer and follow the prompts.

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macOS and Linux

  1. Open a terminal window.
  2. Download the installer using curl or wget or your favorite program and run: 
    curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh

    or

    wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh
  3. Follow the prompts to complete the installation.
  4. Close and reopen your terminal to apply the changes.

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If you encounter any issues, please refer to the official Mamba documentation.

If you’re unable to install Mamba, you can alternatively install Conda as a fallback option. If you choose this route, remember to replace mamba with conda in all subsequent commands in these instructions.

After you have a working Mamba (or Conda) installation you can proceed to create and activate the environment.

Creating the Environment

  1. Open your terminal:
    • On Windows: Open “Miniforge Prompt” or “Command Prompt”.
    • On macOS/Linux: Open your regular terminal.
  2. Navigate to your lesson workspace directory.
  3. Run the following command:
mamba env create -f https://raw.githubusercontent.com/esciencecenter-digital-skills/medical-image-processing/main/learners/environment.yml
  1. Wait for the installation to complete. This may take a few minutes.

Activating the Environment

After the installation is complete, activate the environment:

mamba activate medical_image_proc

Downloading Image Files from Zenodo

Now that you have the environment set up, let’s download the necessary files from Zenodo.

Option A: Manual Download

  1. Open your web browser and navigate to this lesson’s data Zenodo record address.
  2. Look for the “Files” section on the page.
  3. Click tha download button for downloading the data.zip file containing the images.
  4. Once downloaded, extract the contents of the ZIP file.
  5. Move the extracted folder to your lesson workspace directory (where you’ll create notebooks and work during the lesson).

Option B: Using Zenodo API

You can use the Zenodo API to download the files. The zenodo_get package should already be installed in your environment.

  1. Ensure you’re in your lesson workspace directory and your medical_image_proc environment is activated.
  2. Use the following command in your terminal (Miniforge Prompt if you are a Windows user): 
    zenodo_get 13311246
  3. Extract the contents of data.zip.

Remember to keep the medical_image_proc environment activated throughout the entire lesson. If you close your terminal or restart your computer, you’ll need to activate the environment again using the activation command above.

Also, remember to verify that the extracted folder is in your lesson workspace directory (where you’ll create notebooks and work during the lesson).