Quantitative Big Imaging Course 2015
Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2015 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging. The lectures have been prepared and given by Kevin Mader, Anders Kaestner, and Marco Stampanoni. Please note the Lecture Slides and PDF do not contain source code, this is only available in the handout file.
Lectures
- 19th February - Introduction and Workflows
- 26th February - Image Enhancement (A. Kaestner)
- 5th March - Basic Segmentation, Discrete Binary Structures
- 12th March - Advanced Segmentation
- 19th March - Applications Graphical Models and Machine Learning (A. Lucchi)
- 26th March - Analyzing Single Objects
- 2nd April - Analyzing Complex Objects
- 16th April - Spatial Distribution
- 23rd April - Statistics and Reproducibility
- 30th April - Dynamic Experiments
- 7th May - Scaling Up / Big Data
- 21th May - Guest Lecture - Applying Image Processing
- Image registration: a case study on material science - A. Patera: EMPA and Paul Scherrer Institut
- Introduction to High Content Screening - M. Prummer: NEXUS Personalized Medicine Center (formerly Roche)
- Application of High Content Screening - S. Noerrelykke: ScopeM
- 28th May - Project Presentations
- Bacteria-Hyphae interaction in microfluidic channels - Benedict Borer
- Multi-contrast X-ray imaging-based study of aggregates in cement-based mortars - Fei Yang
- Mapping Non/Less-Porliferative Cells in the Adult Zebrafish Tissue - Hanyu Qin
- Dynamic tracking of lithium volume in a lithium-ion battery, using synchrotron X-ray tomographic microscopy - Patrick Pietsch
- Equilibrium Catalyst - Rosh Jacob
Exercises
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 (www.knime.org), with example workflows here (https://www.knime.org/example-workflows). 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 last year (available on: kmader.github.io/Quantitative-Big-Imaging-Course/) are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)
Assistance
The exercises will be supported by Filippo Arcadu, Kevin Mader, and Christian Dietz. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.
Specific Assignements
- 19th February - Introduction and Workflows (Christian Dietz, Intro to KNIME for Image Processing)
- 26th February - Image Enhancement (A. Kaestner)
- KNIME Exercises
- Matlab Exercises (for students experienced in Matlab)
- Starting Data / Matlab Directory
- 5th March - Basic Segmentation, Discrete Binary Structures
- 12th March - Advanced Segmentation and Processing
- 19th March - Machine Learning in Image Processing (A. Lucchi)
- 26th March - Analyzing Single Objects
- 2nd April - Analyzing Complex Objects
- 16th April - Spatial Distribution
- 23rd April - Statistics and Reproducibility
- 30th April - Dynamic Experiments
- 7th May - Scaling Up / Big Data
- 21th May - Guest Lecture - Applying Image Processing
- 28th 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.
Other Material
- Project Signup
- Here you signup for your project with team members and a short title and description
- List
- Course Wiki (For Questions and Answers, discussions etc)
- Performance Computing Courses
- Reprodudible Research Courses