This work is licensed by Robert Haase, ScaDS.AI Dresden/Leipzig under a Creative Commons Attribution 4.0 International License unless mentioned otherwise.

Bio-image Data Science

This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material will develop between April and July 2024 and shared here in this github repository. Corresponding PPTx files can be found on zenodo.

Teaching Goal

Students learn the full workflow of common bio-image data science projects to a degree that they can execute a scientific data analysis project in this context on their own. They will be familiar with common bio-image analysis algorithms and workflows, how to choose them according to a scientific goal, and how to measure quality of derived results. Attending the lecture and executing the practicals qualifies the students to work as bio-image data scientist in the pharmaceutical industry or basic biological research.

Course contents

• Introduction to Bio-image Data Science (Apr 2nd 2024)

  • Basics of microscopy
  • Introduction to Bio-image Analysis
  • Exercises:
    • Setting up a local environment
    • Setting up Jupyter Hub at Scientific Computing / Leipzig University
    • Execute the trailer notebook

• Research Data Management (Apr 9th 2024)

  • Introduction to Research Data Management
    • Research Data Life Cycle
    • Data Management Plans
  • Sharing and licensing
    • The FAIR Principles
    • Creative Common License
    • Open Source Licenses
  • Exercises:
    • Installation
    • Accessing the nextcloud
    • Exploring the BioImage Archive
    • Exploring Meta Data on Zenodo
    • Exploring Meta Data on DOI.org

• Research Software Management and Image Processing

  • Research Software Management
    • Version control with git
    • Conda environments
    • Software quality indicators
  • Image Processing
    • Image visualization
    • Image filtering
    • Morphological operations
  • Exercises:
    • Sending pull-requests on GitHub
    • Image processing
    • Breaking backwards compatibility

• Image Segmenation

  • Binary images
    • Otsu Thresholding
    • Morphological operations
  • Labeling algorithms
    • Connected component labeling
    • Voronoi-Labeling
    • Watershed-algorithm
  • Napari
  • Exercises
    • Image segmentation with Python
    • Using Napari with Python
    • Setting up workflows in Napari interactively
    • Exporting notebooks from Napari

Planned contents

• Image Data Management
• Microscopy image analysis (filtering, segmentation, feature extraction)
• Quality Assurance
• Supervised and unsupervised machine learning for pixel, object and image classification / clustering
• Deep Learning techniques for image denoising and segmentation
• Multi-modal Deep Learning + Large Language Models for bioimage analysis
• Prompt-Engineering

Pre-requesites

• Basic Python programming skills are required

Literature

• Bioimage Data Analysis Workflows ‒ Advanced Components and Methods. Editors: Kota Miura, Nataša Sladoje. 2022
• A Hitchhiker's guide through the bio-image analysis software universe. Haase et al. FEBS Letters. Volume 596, Issue 19 p. 2472-2485
• Bio Image Analysis Notebooks, Haase et al. 2024 DOI: 10.5281/zenodo.10465773

More literature might be added during the lecture.

Covered Python libraries

In this course we will use the following Python libraries to analyse mciroscopy image data

• numpy
• scipy
• scikit-image
• clEsperanto.
• napari
• scikit-learn
• apoc
• OpenAI API
• stardist
• n2v
• cellpose
• seaborn

See also

Former lectures and related materials

A lecture covering similar contents was held in the past years at TU Dresden:

• Bio-image Analysis, programming, bio-statistics and machine learning 2023
• Bio-image Analysis, programming, bio-statistics and machine learning 2022
• Bio-image Analysis, programming, bio-statistics and machine learning 2021
• Bio-image Analysis, programming, bio-statistics and machine learning 2020
• Bio-image Analysis, ImageJ Macro programming 2019
• Bio-image Analysis Notebooks

Image Analysis

• Introduction to Bioimage Analysis
• Basics of Image Processing and Analysis by Kota Miura
• Classic ImageJ training resources
• Bioimage Data Analysis Workflows edited by Kota Miura and Nataša Sladoje

Python

• Python cheat sheet
• Python/Conda environments
• Scientific data analyis in Python, Biotec lecture
• Python for Microscopists, video series by Sreeni
• Managing Conda environments, online documentation
• Bio-image Analysis using Scikit-Image in Python, Biotec lecture
• Python for Bio-image Analysis by the Image Analysis Focused Interest Group of the Royal Microscopical Society
• Interactive Bioimage Analysis with Python and Jupyter, NEUBIAS academy webinar, Part 2
• Multi-dimensional image visualization in Python using napari, NEUBIAS Academy webinar

Contributing

Contributions to this repository are welcome! If you see typos, bugs or have general feedback, please create a github issue to let us know. If you would like to add additional lessons or want to suggest improvements to existing ones, pull-requests are very welcome!

Acknowledgements

Some of the materials in this repository originate from the BioImageAnalysis Notebooks, were written by Robert Haase Guillaume Witz and were licensed CC-BY 4.0. Robert Haase acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence Physics of Life of TU Dresden. We acknowledge the financial support by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research „Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig“, project identification number: ScaDS.AI

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