부산대학교 도서관

Intracranial aneurysms are pathologic dilations of the vessel wall, which bear the risk of rupture and of fatal consequences for the patient. Since treatment may be accompanied by severe complications as well, rupture risk assessment and thus rupture risk prediction plays an important role in clinical research. In this work, we investigate the potential of morphological features for rupture risk status classification in 100 intracranial aneurysms. We propose a pipeline for morphological feature extraction and rupture status classification with subsequent feature ranking and inspection. Our classification setup involves training separate models for each aneurysm type (sidewall or bifurcation) with multiple learning algorithms. We report on the classification performance of our pipeline and examine the predictive power of each morphological parameter towards rupture status classification. Further, we identify the most important features for the best models and study their marginal prediction.