Artificial intelligence based analysis of cerebral angiograms for aneurysm diagnosis and prognosisBizjak, Žiga (Avtor) Špiclin, Žiga (Mentor) Intracranial aneurysmSegmentationDeep learningIntracranial vesselsRupture risk predictionVascular diseases represent a significant global health concern, ranking as the foremost cause of disability and mortality worldwide. Accounting for approximately 32 % of all deaths, these diseases predominantly impact the cardiac and cerebral vasculatures. Among the spectrum of cerebrovascular pathologies, intracranial aneurysms (IA) emerge as a prevalent concern, manifesting as balloon-like protrusions from weakened vessel segments. IA affect 2 % to 8 % of the global population and, if ruptured, can result in stroke, a condition that is both serious and life-threatening. The management of IA encompasses crucial steps such as detection, isolation, morphological measurements, rupture risk assessment, and growth evaluation. Presently, skilled radiologists manually perform these tasks in clinical settings, introducing the potential for intra- and inter-rater variability. The automation of these processes holds promise for enhancing IA management and furnishing valuable insights for deciding between surgical intervention and follow-up imaging, a determination typically based on rupture risk assessment. This doctoral thesis focuses on the creation and validation of modality-independent computer-aided methods for IA management. Our initial step involves utilizing deep learning to segment intracranial vessels across various angiographic modalities. Following that, we transformed these segmented vessels into trigonometric meshes, a format that is modality-independent. Subsequent steps, including detection, isolation, and rupture prediction, are executed on these meshes using innovative deep-learning algorithms to attain state-of-the-art outcomes throughout the entire process. Moreover, in the case of a follow-up approach, we have devised an algorithm for evaluating intracranial growth between two consecutive imaging sessions. The development and evaluation of these methodologies were carried out using a substantial dataset comprising over 2000 CTA and 1000 MRA images sourced from multiple hospitals. Our algorithms furnish clinicians with critical information at each stage of IA management, thereby mitigating intra- and inter-rater variability and elevating the standard of patient care.20242024-07-01 07:40:12Doktorsko delo/naloga159117sl