Quantitative Big Imaging Course 2020

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Important information: The QBI lectures continue also with corona! Online alternatives are offered.

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Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2020 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging.

The lectures were orignally prepared by Kevin Mader and are now given by Anders Kaestner 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 2020 Playlist. The lectures are meant to be followed in chronological order and each lecture has a corresponding hands-on exercise. The entire lecture set is available as a single PDF file available in the releases section

Learning Objectives

General

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

Segmentation

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 Anders Kaestner directly (anders.kaestner@psi.ch).

• 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.

Detailed information

• Weekly lecture plan
• Exercises
• Communication and Feedback
• Software dependencise
• Other relevant material

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 Anders 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.

• Practice Questions

Projects

The projects are optional, but highly recommended to do as they give better insight in practical problems occurring while analyzing real data. We provide a list of projects, but you can also take the opportunity to extract a fraction of your other ongoing projects like master's or PhD project. Please contact Anders Kaestner in advance if you choose to define your own project.

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