Python BioImage Analysis Tutorial

originally created in 2016
updated and converted to a Jupyter notebook in 2017
updated and converted to python 3 in 2018
by Jonas Hartmann (Gilmour group, EMBL Heidelberg)

Note that an updated an extended version of this tutorial is being developed and will hopefully be released in 2022. 🤞

Aims and Overview

This tutorial teaches the basics of bio-image processing, segmentation and analysis in python. It integrates explanations and exercises in a (hopefully) self-explanatory fashion, enabling participants to build their own image analysis pipelines step by step.

The tutorial uses single-cell segmentation of 2D confocal fluorescence microscopy images to illustrate key concepts from preprocessing to segmentation to (very basic) data analysis. It concludes with a small section on how to apply such a pipeline to multiple images at once (batch processing).

Everything you need to know to get started can be found in the jupyter notebook image_analysis_tutorial.ipynb. To find out more about how to run these materials interactively, see the Jupyter documentation.

Note that this tutorial was part of a course aimed at people with basic knowledge of python. The course included introductory sessions/lectures on scientific python (in particular numpy and matplotlib) as well as on image analysis (see the slides in this repository). For those tackling this tutorial on their own, it is therefore recommended to first acquire basic scientific python knowledge elsewhere (e.g. at python-course.eu).

Content Overview

Lecture
- Working with digital images
  - Images as arrays of numbers
  - Look-up tables (LUTs)
  - Dimensions
  - Bit-depth
- Image analysis pipelines
  - Preprocessing: filters, kernels, convolution, background subtraction
  - Foreground detection: thresholding, morphological operations
  - Segmentation: labels, seeds, watershed
  - Postprocessing: object filtering
  - Making measurements
Tutorial
- Importing Modules & Packages
- Loading & Handling Image Data
- Preprocessing
- Manual Thresholding & Threshold Detection
- Adaptive Thresholding
- Improving Masks with Binary Morphology
- Connected Components Labeling
- Cell Segmentation by Seeding & Expansion
- Postprocessing: Removing Cells at the Image Border
- Identifying Cell Edges
- Extracting Quantitative Measurements
- Simple Analysis & Visualization
- Writing Output to Files
- Batch Processing

Old Versions and Other Sources

This was part of the EMBL Bio-IT/ALMF Image Analysis with Python 2018 course (see the EMBL Gitlab repo).

If you are looking for the python 2 version from 2017, see the 2017_legacy_python_version branch or the corresponding EMBL GitLab repo.

The original 2016 materials can be found in Karin Sasaki's corresponding Github repo.

Acknowledgements

The first version of this tutorial was created for the EMBL Python Workshop - Image Processing course organized by Karin Sasaki and Jonas Hartmann in 2016. Additional lecturers and TAs contributing to this course were Kota Miura, Volker Hilsenstein, Aliaksandr Halavatyi, Imre Gaspar, and Toby Hodges.

The second installment (the EMBL Bio-IT Image Processing Course, 2017) was organized and taught by Jonas Hartmann and Toby Hodges.

The third version of this tutorial was part of the EMBL Bio-IT/ALMF Image Analysis with Python 2018 course, organized by Jonas Hartmann and Toby Hodges in collaboration with Tobias Rasse and Volker Hilsenstein. Additional organizational help came from Christian Tischer and Malvika Sharan.

Many thanks to all the helpful collaborators and the interested students who were instrumental in making these courses a success.

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