Lecture: Probabilistic Shape Modelling

Course Number 43075-01
Lecturers Marcel Luethi
Assistants Ghazi Bouabene
Time and Location Tue 14:15 - 16:00; Seminarraum 05.002, Spiegelgasse 5
Start 27-02-2018
Exercises Tue 16:15 - 18:00; Seminarraum 05.002, Spiegelgasse 5
Prerequisites Open to Master and PhD students with basic knowledge in probability theory and statistics, linear algebra as well as programming experience in a modern programming language (e.g. Java or C++)
Objectives At the end of the course the students should be able to
- describe how medical images can be analysed using Shape models.
- apply the mathematical concept of a Gaussian process to model anatomical shapes
- understand Bayesian approaches to medical image analysis
- to develop programs for medical image analysis using the open source software scalismo
Contents Statistical shape models are one of the most important technologies in computer vision and medical image analysis. With this technology, the computer learns the characteristic shape variations of an object or organ. The model resulting from this analysis may then be used in implant design, image analysis, surgery planning and many other fields.

In this course, you will get insights from mathematics, statistics and machine learning, in order to address practical problems, as well as a theoretical and practical introduction to the open source software Scalismo. This software is used today for the automatic detection of organs in medical images or the design of medical implants. You will come to a point where you can use your acquired skills and knowledge for real-world professional applications or academic research.
Literature Links to related literature will be given as part of the online course.
Assessment Lehrveranst.-begleitend

Please note: The final grade will be computed based on the result of 2 practical projects and an oral exam.
Each project contributes 25% to the final grade and the oral exam 50%.
Credit Points 6
Grades 1-6 0,5
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