Automatic neck plane detection and 3D geometric characterization of aneurysmal sacs.

Geometric indices defined on intracranial aneurysms have been widely used in rupture risk assessment and surgical planning. However, most indices employed in clinical settings are currently evaluated based on two-dimensional images that inevitably fail to capture the three-dimensional nature of complex aneurysmal shapes. In addition, since measurements are performed manually, they can suffer from poor inter and intra operator repeatability. The purpose of the current work is to introduce objective and robust techniques for the 3D characterization of intracranial aneurysms, while preserving a close connection to the way aneurysms are currently characterized in clinical settings. Techniques for automatically identifying the neck plane, key aneurysm dimensions, shape factors, and orientations relative to the parent vessel are demonstrated in a population of 15 sidewall and 15 terminal aneurysms whose surface has been obtained by two trained operators using both level-set segmentation and thresholding, the latter reflecting typical clinical practice. Automatically-identified neck planes are shown to be in concordance with those manually positioned by an expert neurosurgeon, and automatically-derived geometric indices are shown to be largely insensitive to segmentation method or operator. By capturing the 3D nature of aneurysmal sacs and by minimizing observer variability, our approach allows large retrospective and prospective studies on aneurysm geometric risk factors to be performed using routinely acquired clinical images.

RCT Entities:   Population Interventions Outcomes