Computational Physiology for Medical Image Computing - IN2319
Organization
Lecture:
Prof. Bjoern Menze,
Dr. Kuangyu Shi and Esther Alberts, Miguel Molina, Pedro Gomez, Cagdas Ulas, Markus Rempfler.
Time & Location
- Lecture: Wednesdays, 2:30pm - 4:00pm in room MI 03.09.012
- Work on Case Studies (Exercises): Wednesdays, 4:00pm - 5:30pm in room MI 03.09.012
Overview
Upon completion of the course, participants will be able to use computational models for extracting diagnostic information from different types of clinical image data sets. These data sets may provide information, for example, about blood perfusion or micro-structural tissue properties, about metabolic processes, or patterns of disease progression. The participants will understand the physiological concepts underlying the computational algorithms employed, and will know of advantages and shortcomings of different modeling strategies. This will allow them to analyze clinical imaging protocols with respect to the underlying physiological information, and to propose diagnostic algorithms that combine anatomical and physiological information of different imaging. A focus will be on applications from neuroimaging.
During the first part of the course, participants will implement and evaluate small case studies in team of up to three people. The use of public Python libraries, such as
NiPy and
NiPype, is encouraged. Towards the end of the course, teams will demonstrate their work to other course participants in a short oral presentation and in a short summary paper.
Prerequisites
- Basic knowledge in image computing (e.g., CAMP I lecture or related courses).
- Interest in algorithmic/implementation aspects of signal processing and image computing tasks.
- Specific knowledge in Python is not required. However, you should be willing to acquire skills during the course.
Regulations
- Module Specification: IN2319
- SWS: 2+2
- ECTS: 6 Credits
- Course Language: English
- Exam: presentation of course project, and oral exam (20 minutes)
Course Schedule
NB: ZIP files of the lecture slides can be unzipped using 'CpMic16'.
Literature
Pointers to freely available literature are given at the end of the lecture slides.
Case studies
Topic and tutor:
Send an email to Bjoern Menze with your two favourite topics, mention 'CPMIC lecture' in the subject.
