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kmader / Quantitative-Big-Imaging-2019

The material for the Quantitative Big Imaging course at ETHZ for the Spring Semester 2019
http://kmader.github.io/Quantitative-Big-Imaging-2019
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Quantitative Big Imaging Course 2019 Binder with JupyterLab

Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2019 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 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 Kevin Mader directly (mader@biomed.ee.ethz.ch).

Slack

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

Weekly Plan

21st February - Introduction and Workflows

Exercises

28th February - Ground Truth: Building and Augmenting Datasets

Exercises

7th March - Image Enhancement (Guest Lecture - A. Kaestner)

Exercises

14th March - Basic Segmentation, Discrete Binary Structures

Exercises

21th March - Advanced Segmentation

Exercises

28th March - Analyzing Single Objects, Shape and Texture

Exercises

4th April - Analyzing Complex Objects

Exercises

11th April - Dynamic Experiments

Exercises

18th April - Statistics, Prediction, and Reproducibility

Exercises

2nd May - Scaling Up / Big Data

Exercises

9th May - Guest Lecture - High Content Screening (M. Prummer)

Exercises

16th May - Tracking/Dynamic Experiments - Live Coding

23rd May - 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 two years ago (available here are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)

Install KNIME

Install Python

If you use colab, kaggle or mybinder you won't need python on your own machine but if you want to set it up in the same way the class has you can follow the instructions shown in the video here and below

  1. Install Anaconda Python https://www.anaconda.com/distribution/#download-section
  2. Download the course from github as a zip file
  3. Extract the zip file
  4. Open a terminal (or command prompt on windows)
  5. Go to the binder folder inside the course directory (something like: Downloads/Quantitative-Big-Imaging-2019-master/binder)
  6. Install the environment
  7. conda env create -f environment.yml
  8. Activate the environment conda activate qbi2019 or activate qbi2019
  9. Go up one directory to the root of the course cd ..
  10. Start python jupyter notebook

Assistance

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.

Feedback (as much as possible)

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.

Projects

Software Dependencies

The course, slides and exercises are primarily done using Python 3.6 and Jupyter Notebook 5.5. The binder/repo2docker-compatible environment](https://github.com/jupyter/repo2docker) 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 https://hub.docker.com/r/kmader/qbi2018/

All Lectures

The packages which are required for all lectures

Machine Learning Packages

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

Image Registration / Medical Image Data

For the image registration lecture and medical image data

Other Material

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