“We extract 3D geometries of Intracranial aneurysms from medical imaging data and describe their shapes by various radiomic, morphometric and psychometric methods. Using statistical analysis and machine learning, we seek to assess the clinical significance of Intracranial aneurysms morphology”
Title: Shape-based analysis of intracranial aneurysms
Supervisors:
Prof. Dr. Vartan Kurtcuoglu (UZH)
Prof. Dr. Sven Hirsch (ZHAW)
Keywords: medical data science, statistical analysis, machine learning, 3D medical imaging, radiomics, morphometry, psychometry
Abstract: Intracranial aneurysms (IAs) are focal deformations of brain arteries that occur in 2-5% of the population. Although mostly stable and symptom free, IAs may continue to grow and eventually rupture (incidence rate of about 1% per year). IA rupture is the principal cause for non-traumatic subarachnoid hemorrhage, known for its potentially devastating effects on the patient. Shape plays an important role in the assessment of unruptured IAs. When weighing the risks of rupture against the risks of treatment, radiologists are taking into account IA morphology, but only very qualitatively. In our work, we focus on identifying clinically relevant morphological characteristics with the aim of establishing quantitative shape predictors for IA disease status. We extract 3D geometries of IAs from medical imaging data and describe their shapes by various radiomic, morphometric and psychometric methods. Using statistical analysis and machine learning, we seek to assess the clinical significance of IA morphology.
Dates: Start: August 2015, End: January 2020
List of publications:
https://www.zhaw.ch/en/about-us/person/juch/