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43075-01 - Vorlesung: Probabilistic Shape Modelling (6 KP)
| Semester | Frühjahrsemester 2020 |
| Weitere Semesterveranstaltungen zu diesen KP |
43075-01 (Vorlesung) 43075-02 (Übung) |
| 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 |
| Teilnahmevoraussetzungen | 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 | Wochentag | Zeit | Raum |
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Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
| 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) |
| Prüfung | Lehrveranst.-begleitend |
| Hinweise zur Prüfung | 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 oral exam 50%. Oral exams: 2 July 2020, online. |
| An-/Abmeldung zur Prüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Fachbereich Informatik |