Learn modern methods that will help shaping the future of medical interventions
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 free online 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.
What topics will you cover?
- Modelling families of anatomical shapes
- Basic theory of Gaussian Processes for modelling shape variations
- Usage of the shape modelling software Scalismo
- Building classical statistical shape models from example data
- Reconstruction of partially observed shapes
- Fitting statistical shape models to surfaces
- Establishing point-to-point correspondence between example shapes
- Fitting statistical shape models to images
When would you like to start?
Date to be announced
Who is the course for?
This course is intended for students and professionals with a Bachelor in computer science, medical imaging professionals and biological anthropologists, who are interested in top-notch research, scientific insights and a useful application.
Although you can watch the videos, read the articles, and complete the tests and quizzes on mobile devices such as smartphones or tablets, you will have to install the free software Scalismo on your own workstation in order to use it – there is no online version available.
In order to be able to do this, your computer should meet the following minimum system requirements: Windows (32bit/64bit), Mac OS X or Linux (64bit), 4GB of RAM, 500MB of free HD space. There are no special requirements for the graphic adapter.
Who will you learn with?
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You can use the hashtag #FLstatisticalshapes to talk about this course on social media.