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| Semester | Frühjahrsemester 2018 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende | Marcel Lüthi (marcel.luethi@unibas.ch, BeurteilerIn) |
| Inhalt | 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. |
| 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 | 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 (Master Computer Science) Modul Concepts of Machine Intelligence (Master Studienfach: Computer Science) Modul Methods of Machine Intelligence (Master Computer Science) Modul Praxis aktueller Informatikmethoden (Master Studienfach: Informatik (Studienbeginn vor 01.08.2016)) Wahlbereich Master Informatik: Empfehlungen (Master Informatik (Studienbeginn vor 01.08.2016)) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | 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%. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| 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 |