lxh-123/quantitative-big-imaging-2016

The material for the Quantitative Big Imaging Course at ETHZ

Quantitative Big Imaging Course 2016

Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2016 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging.

The lectures have been prepared and given by Kevin Mader and guest lecturer Anders Kaestner. 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.

Lectures

• 25th February - Introduction and Workflows
• Lecture Slides
• Old Lecture Handout
• 3rd March - Image Enhancement (A. Kaestner)
• Lecture Slides
• Lecture Video: Part 1, Part 2
• 10th March - Basic Segmentation, Discrete Binary Structures
• Lecture Slides
• Lecture Handout as Old PDF
• Lecture Video: Part 1, Part 2
• 17th March - Advanced Segmentation
• Lecture Slides
• Lecture Handout as Old PDF
• Lecture Video: Part 1, Part 2
• 24th March - Analyzing Single Objects
• Lecture Slides
• Lecture Handout as Old PDF
• Lecture Video: Part 1, Part 2
• 7th April - Analyzing Complex Objects
• Lecture Slides
• Lecture Handout
• Lecture Video: Part 1, Part 2
• 14th April - Many Objects and Distributions
• Lecture Slides
• Lecture Handout as PDF
• Lecture Video: Part 1, Part 2
• 21st April - Statistics and Reproducibility
• Lecture Slides
• Lecture Handout as PDF
• Lecture Video: Part 1, Part 2, Part 3
• 28th April - Dynamic Experiments
• Lecture Slides
• Old Lecture Handout as PDF
• Lecture Video: Part 1
• 12th May - Scaling Up / Big Data
• Lecture Slides
• Old Lecture Handout as PDF
• 19th May - Guest Lecture - Applications
• High Content Screening Slides - Michael Prummer / Nexus / Roche
• Roads from Aerial Images Slides - Javier Montoya / Computer Vision / ScopeM
• 26th May - Guest Lecture - Machine Learning and Deep Learning (A. Lucchi)
• Deep Learning Slides
• 2nd June - Project Presentations

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-2015/) 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 Yannis Vogiatzis, Kevin Mader, and Christian Dietz. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.

Specific Assignments

• 25th February - Introduction and Workflows (Christian Dietz, Intro to KNIME for Image Processing)
• Setup
• 26th February - Image Enhancement (A. Kaestner)
• KNIME Exercises
• Starting Data / Matlab Directory
• For students experienced in Matlab they can be found here Matlab Exercises
• For students experienced in Python there is an Jupyter notebook with the same exercises as Matlab Jupyter Notebook or download
• 10th March - Basic Segmentation, Discrete Binary Structures
• KNIME Exercises
• Workflows
• 17th March - Advanced Segmentation
• KNIME Exercises
• IPython Exercises, and IPython Solutions/Advanced but these are still incomplete
• 17th March - Analyzing Single Objects
• KNIME Exercises
• Creating Meshes/STL Models
• 7th April - Analyzing Complex Objects
• KNIME Exercises
• Paraview Curvature
• IPython Notebook (Under development)
• 14th April - Groups of Objects and Distributions
• KNIME Exercises
• IPython Notebook (Under development)
• 21th April - Statistics and Reproducibility
• KNIME Exercises
• 28th April - Dynamic Experiments
• KNIME Exercises
• 12th May - Scaling Up / Big Data
• KNIME / Spark Exercises
• 19th May - Guest Lecture Applications
• KNIME Exercises
• KNIME Workflow
• IPython Notebook
• 26th May - Deep Learning with Aerial Images
• Python Data
• IPython Notebook

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.

• Practice Questions

Projects

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

Other Material

• Course Wiki (For Questions and Answers, discussions etc)
• Main Page
• Performance Computing Courses
• High Performance Computing for Science and Engineering (HPCSE) I
• Introduction to GPU Programming
• Programming Massively Parallel Processors with CUDA
• Reprodudible Research Courses
• Course and Tools in R
• Coursera Course