Cardiovascular and circulatory diseases are the world's leading cause of disability and mortality with their impact having increased at an alarming rate of 22.6% over the past two decades. The WHO statistics from 2008 show that cerebral vessels are among the most affected with 30% of all deaths caused by cerebrovascular pathologies. One such pathology is an intracranial aneurysm (IA), which is formed when a weakened part of cerebral arterial wall bulges into a balloon-like structure. The IA may eventually rupture and lead to subarachnoid hemorrhage, a serious health condition with a high mortality rate. Rupture is fatal in about 40% of cases, of those who survive about 66% suffer from permanent neurological deficit. Rupture is still rather rare as it is estimated that 50 to 80 percent of IAs do not rupture during a lifetime, but a staggering 3.2% prevalence of unruptured IAs (1 in 30 people) still leads 500,000 people to die worldwide each year due to rupture and half are younger than 50. The estimated overall direct and indirect costs of the treatment are 138 million USD per year. Clearly, there is a huge demand for constant improvement of tools and methods for clinical management of IAs. Although treatment options like neurosurgical clipping or endovascular coiling are well established for large (dome height>10 mm) and symptomatic IAs, there is an urgent need to improve clinical management of smaller IAs. These are often asymptomatic and discovered incidentally using 3D-DSA, CTA or MRA imaging, whereas corresponding treatment risk/benefit ratio is strongly in favor of the "no treatment" option, since small IAs rupture more frequently during treatment than larger ones. Similar considerations arise in the management of treated IAs irrespective of their size, where the prognostic factors and clinical guidelines to prevent a rare, but potentially fatal recurrence or rebleeding are yet to be established. Recent studies indicate that in-vivo 3D-DSA, CTA or MRA based morphologic measurements such as aneurysm size, aspect ratio (dome height/neck width), aneurysm-to-vessel size ratio and other shape indices are important independent factors contributing to high risk of rupture. Compared to hemodynamic indices like wall shear stress and pulsatility index, the morphologic indices proved more reliable for estimating rupture risk of large aneurysms. The morphologic indices mainly focus on large saccular IAs, while they are rather unspecific for small IAs due to their gross shape description. A recent study indicated that the risk is much higher for IAs that grow over time, irrespective of the initial size. Thus, novel and better risk factors may be established from longitudinal 3D-DSA, CTA or MRA images by quantifying subtle morphologic changes of the observed IA. The main goal of the proposed project is to develop innovative methods and systems based on in-vivo imaging in order to detect and diagnose IAs and perform pre- and post- treatment assessment and follow-up using quantitative morphologic descriptors. All the theoretical, computational and translational activities will be concentrated around the following themes: 1) develop accurate and reliable modality-independent (3D-DSA, CTA or MRA) detector using advanced convolutional neural networks so as to capture small IAs as early as possible; 2) develop novel methods for vasculature segmentation and IA isolation from parent vessels, and novel, more descriptive morphologic measures; 3) develop novel multi-modality deformable registration for normalization of follow-up images, and novel morphologic measures that quantify IA growth; 4) develop standardized validation datasets using real 3D-DSA, CTA or MRA images and perfom objective and rigorous validation of novel methods, and prospectively validate them in clinical screening studies; 5) translate the developed methods and systems into clinical environment and disseminate results into relevant scientific communities. Ultimately, the success of this project will have a tremendous impact on clinical management of intracranial aneurysms.