In this course, we will address a typical problem in medical image analysis using machine learning approaches. We will first analyze the state of the art and then focus on the development of machine-learning-based solutions that support diagnosis and risk assessment for cerebral aneurysms.
Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (SAH) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time.
It is therefore highly desirable to detect aneurysms early and decide about the appropriate rupture prevention strategy. Diagnosis and treatment planning is based on angiographic imaging using MRI, CT, or X-ray rotation angiography.
Major goals in image analysis are the detection and risk assessment of aneurysms. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.
- Trainer/in: Anja Hennemuth
- Trainer/in: Markus Hüllebrand
- Trainer/in: Nina Krüger