Zur Merkliste hinzufügen
Zurück

 

43075-01 - Vorlesung: Probabilistic Shape Modelling 6 KP

Semester Frühjahrsemester 2020
Angebotsmuster Jedes Frühjahrsem.
Dozierende Marcel Lüthi (marcel.luethi@unibas.ch, BeurteilerIn)
Inhalt Statistical shape models are an important technology 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, we will combine methods from mathematics, statistics and machine learning to build probabilistic models of shape variation. In particular we will formulate shape modelling in a Bayesian setting, using Gaussian processes and Markov-Chain Monte Carlo methods as the core theoretical concepts.

We will apply our methods in two real-world projects: The 3D reconstruction of partial bones, and the automatic analysis of femur-bone shapes in 3D CT-images. Both theory and practice are supported by exercises using the open source software Scalismo. This software library is used today in the industry for the automatic detection of organs in medical images or the design of medical implants.
Lernziele 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
Literatur Links to related literature will be given as part of the online course.
Bemerkungen Requires participation in the online course "Shape Modelling - Computing the human anatomy" (www.futurelearn.com/courses/statistical-shape-modelling)
Weblink Course Webpage, online course

 

Teilnahmebedingungen 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++)
Unterrichtssprache Englisch
Einsatz digitaler Medien Online-Angebot obligatorisch
HörerInnen willkommen

 

Intervall wöchentlich
Datum 18.02.2020 – 26.05.2020
Zeit Dienstag, 14.15-16.00 Spiegelgasse 5, Seminarraum 05.002
Datum Zeit Raum
Dienstag 18.02.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 25.02.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 03.03.2020 14.15-16.00 Uhr Fasnachtsferien
Dienstag 10.03.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 17.03.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 24.03.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 31.03.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 07.04.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 14.04.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 21.04.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 28.04.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 05.05.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 12.05.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 19.05.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 26.05.2020 14.15-16.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Module Doktorat Informatik: Empfehlungen (Promotionsfach Informatik)
Modul: Applications of Distributed Systems (Masterstudium: Computer Science)
Modul: Concepts of Machine Intelligence (Master Studienfach Computer Science)
Modul: Methods of Machine Intelligence (Masterstudium: Computer Science)
Leistungsüberprüfung Lehrveranst.-begleitend
Hinweise zur Leistungsüberprüfung The final grade will be computed based on the result of 2 practical projects and a written exam.
Each project contributes 25% to the final grade and the written exam 50%.
An-/Abmeldung zur Leistungsüberprüfung An-/Abmelden: Belegen resp. Stornieren der Belegung via MOnA
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,5
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch
Anbietende Organisationseinheit Fachbereich Informatik

Zurück