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Quantitative Big Imaging Course 2018

Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2018 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging.

The lectures have been prepared and given by Kevin Mader and associated guest lecturers. Please note the Lecture Slides and PDF do not contain source code, this is only available in the handout file. Some of the lectures will be recorded and placed on YouTube on the QBI Playlist. The lectures are meant to be followed in chronological order and each lecture has a corresponding hands-on exercises in the exercises section.


The lecture focuses on the challenging task of extracting robust, quantitative metrics from imaging data and is intended to bridge the gap between pure signal processing and the experimental science of imaging. The course will focus on techniques, scalability, and science-driven analysis.

Learning Objectives


  1. Ability to compare qualitative and quantitative methods and name situations where each would be appropriate
  2. Awareness of the standard process of image processing, the steps involved and the normal order in which they take place
  3. Ability to create and evaluate quantitative metrics to compare the success of different approaches/processes/workflows
  4. Appreciation of automation and which steps it is most appropriate for
  5. The relationship between automation and reproducibility for analysis

Image Enhancement

  1. Awareness of the function enhancement serves and the most commonly used methods
  2. Knowledge of limitations and new problems created when using/overusing these techniques


  1. Awareness of different types of segmentation approaches and strengths of each
  2. Understanding of when to use automatic methods and when they might fail

Shape Analysis

  1. Knowledge of which types of metrics are easily calculated for shapes in 2D and 3D
  2. Ability to describe a physical measurement problem in terms of shape metrics
  3. Awareness of common metrics and how they are computed for arbitrary shapes

Statistics / Big Data

  1. Awareness of common statistical techniques for hypothesis testing
  2. Ability to design basic experiments to test a hypothesis
  3. Ability to analyze and critique poorly designed imaging experiments
  4. Familiarity with vocabulary, tools, and main concepts of big data
  5. Awareness of the differences between normal and big data approaches
  6. Ability to explain MapReduce and apply it to a simple problem

Target Audience

The course is designed with both advanced undergraduate and graduate level students in mind. Ideally students will have some familiarity with basic manipulation and programming in languages like Python (Matlab or R are also reasonable starting points). Much of the material is available as visual workflows in a tool called KNIME, although these are less up to date than the Python material. Interested students who are worried about their skill level in this regard are encouraged to contact Kevin Mader directly (

  • Students with very diverse academic backgrounds have done well in the course (Informatics to Art History to Agriculture).
  • Successful students typically spent a few hours a week working on the exercises to really understand the material.
  • More advanced students who are already very familiar with Python, C++, or Java are also encouraged to take the course and will have to opportunity to develop more of their own tools or explore topics like machine learning in more detail.


For communicating, discussions, asking questions, and everything, we will be trying out Slack this year. You can sign up under the following link. It isn't mandatory, but it seems to be an effective way to engage collaboratively How scientists use slack


22th February - Introduction and Workflows

1st March - Image Enhancement (Guest Lecture - A. Kaestner)

8th March - Basic Segmentation, Discrete Binary Structures

15th March - Advanced Segmentation

22th March - Supervised Problems and Segmentation

29th March - Analyzing Single Objects

12th April - Advanced Shape and Texture

19th April - Analyzing Complex Objects and Distributions

26th April - Dynamic Experiments

3rd May - Statistics, Prediction, and Reproducibility

17th May - Guest Lecture - High Content Screening (M. Prummer)

24th May - Scaling Up / Big Data

31st May - Project Presentations

Additional Lectures from Previous Years

Tutorial: Python, Notebooks and Scikit

Roads from Aerial Images

Javier Montoya / Computer Vision / ScopeM

Introduction to Deep Learning / Machine Learning

Presented by Aurelien Lucchi in Data Analytics Lab in D-INFK at ETHZ


General Information

The exercises are based on the lectures and take place in the same room after the lecture completes. The exercises are designed to offer a tiered level of understanding based on the background of the student. We will (for most lectures) take advantage of an open-source tool called KNIME (, with example workflows here ( The basic exercises will require adding blocks in a workflow and adjusting parameters, while more advanced students will be able to write their own snippets, blocks or plugins to accomplish more complex tasks easily. The exercises from two years ago (available here are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)


The exercises will be supported by Amogha Pandeshwar and Kevin Mader. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.

Online Tools

The exercises will be available on Kaggle as 'Datasets' and we will be using mybinder as stated above. For those interested there will be an option to use Github Classroom to turn in assignments (make sure your address is linked to your github account)

Specific Assignments

22rd February - Introduction and Workflows

1st March - Image Enhancement (A. Kaestner)

  • For all exercises it is important to take the starting data
  • Starting Data


Binder (Python)

For students experienced in Python there are the binder Notebooks

Kaggle (Python)

8th March - Basic Segmentation, Discrete Binary Structures


Hard Exercises

15th March/22nd March - Advanced Segmentation / Supervised Segmentation


29th March - Analyzing Shapes


12th April - Advanced Shape and Texture Analysis

19th April - Many Objects and Distributions

26th April - Dynamic Experiments

3rd May - Statistics, Prediction, and Reproducibility

17th May - Guest Lecture - High Content Screening (M. Prummer) / Project Presentations

24th May - Scaling Up / Big Data

31st May - Project Presentations

Feedback (as much as possible)

  • Create an issue (on the group site that everyone can see and respond to, requires a Github account), issues from last year
  • Provide anonymous feedback on the course here
  • Or send direct email (slightly less anonymous feedback) to Kevin

Final Examination

The final examination (as originally stated in the course material) will be a 30 minute oral exam covering the material of the course and its applications to real systems. For students who present a project, they will have the option to use their project for some of the real systems related questions (provided they have sent their slides to Kevin after the presentation and bring a printed out copy to the exam including several image slices if not already in the slides). The exam will cover all the lecture material from Image Enhancement to Scaling Up (the guest lecture will not be covered). Several example questions (not exhaustive) have been collected which might be helpful for preparation.


  • Overview of possible projects
  • Here you signup for your project with team members and a short title and description

Software Dependencies

The course, slides and exercises are primarily done using Python 3.6 and Jupyter Notebook 5.5. The binder/repo2docker-compatible environment]( can be found at binder/environment.yml. A full copy of the environment at the time the class was given is available in the wiki file. As many of these packages are frequently updated we have also made a copy of the docker image produced by repo2docker uploaded to Docker Hub at

All Lectures

The packages which are required for all lectures

  • numpy
  • matplotlib
  • scipy
  • scikit-image
  • scikit-learn
  • ipyvolume

Machine Learning Packages

For machine learning and big data lectures a few additional packages are required

  • tensorflow
  • pytorch
  • opencv
  • dask
  • dask_ndmeasure
  • dask_ndmorph
  • dask_ndfilter

Image Registration / Medical Image Data

For the image registration lecture and medical image data

  • itk
  • SimpleITK
  • itkwidgets

Other Material

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