Weekly Plan

20th February - Introduction and Workflows

• Slides (static) Lecture Handout

  Lecture Videos

• Part 1
• Part 2

Exercises

• Getting started with python for image processing
• Getting Started with KNIME

  Supporting material for the exercises

• Introduction to Python and Jupyter for Data-Science

27th February - Image Enhancement

• Slides (static) Lecture Handout

• Slides on non-local means (static) Lecture Handout
• There was a recording problem on this lecture, the 2018 Lecture Video (A. Kaestner) can be used as substitute.

Exercises

• Overview part 1-3
• Overview part 4
• 

5th March - Ground Truth: Building and Augmenting Datasets

• Slides (static) Lecture Handout

Lecture Videos

• Part 1
• Part 2

Exercises

• Data augmentation exercise

12th March - Basic Segmentation, Discrete Binary Structures

• Part 1: Slides (static) Lecture Handout
• Part 2: Slides (static) Lecture Handout

Lecture videos

• Part 1
• Part 2

Exercises

• Overview

19th March - Advanced Segmentation

• Part 1: Advanced segmentation Slides (static) Lecture Handout

• Part 2: Supervised segmentation Slides (static) Lecture Handout

Lecture Videos

• Part 1: Advanced segmentation
• Part 2: Supervised segmentation approaches

Exercises

• Overview

26th March - Analyzing Single Objects, Shape and Texture

• Part 1: Slides (static) Handout

• Part 2: Slides (static) Handout

Lecture videos

• Part 1: Shape analysis
• Part 2: Advanced Shape analysis and Textures

Exercises

• Overview

2th April - Analyzing Complex Objects

• Slides (static) Lecture Handout

#### Lecture videos

• Part 1: Skeletons
• Part 2: Watershed segmentation

Exercises

• Overview

9th April - Dynamic Experiments

• Slides (static) Lecture Handout

• Feature Points Slides

Lecture videos

• Part 1: Dynamic experiments and object tracking
• Part 2: Registration

Exercises

• Overview

16th April - Easter break

23rd April - Statistics, Prediction, and Reproducibility

• Slides (static) Lecture Handout

Lecture videos

• Part 1: Statistics
• Part 2: Reproducible workflow and visualization

Exercises

• C. Elegans Dataset on Kaggle R Notebook or Python Notebook

• Will come later in python once we know more about Pandas: KNIME Exercises

30th April - Imaging with multiple modalities

• Slides (static) Lecture Handout

Lecture videos

• Part 1: Bimodal imaging
• Part 2: Data frames

Exercises

• Exercises on statistics and data frames

7th May - Scaling Up / Big Data

• Slides (static) Lecture Handout

Lecture videos

• Part 1: Parallel and distributed computing
• Part 2: Databases and DAGs

Exercises

• Kaggle DAG Notebook for Filtering with Tensorflow, this a walk-through exercise. You can play with the number of iterations in the last part to see if there is data transfer bottle neck for GPU compared to CPU.
• Block-based 3D Image Analysis in Dask

14th May - Project Presentations

Info before the presentations

Presentations

| Presenter(s) | Title. | Recording OK | | ————- | ————- |————–| | Cameron Bogon | Analysing Bacteria patterned on surfaces | yes | | Yves Haag, Nemanja Kostic and Tobias Stauffer| Segmentation method to detect microcalcifications | yes | | Jack Kendall| Identification of Distal Radius Fractures using Time-Lapse HR-pQCT Imaging | yes| | Alix Pham, Eylül Ceylan, and Lucien Stöcklin| Cardiomyocyte Segmentation and Morphological Analysis |yes| | Christoph Gäbelein | QBI of Bacteria | yes | | Richardo Carreon Ruiz | 2D Mapping of Ionic Transport in Lithium-ion Batteries: In-situ Quantification | no |