Computer-Aided Detection and Quantification of Intracranial Aneurysms

Early detection, assessment and treatment of intracranial aneurysms is important to prevent rupture, which may cause death. We propose a framework for detection and quantification of morphology of the aneurysms. A novel detector using decision forests, which employ responses of blobness and vesselness filters encoded in rotation invariant and scale normalized frequency components of spherical harmonics representation is proposed. Aneurysm location is used to seed growcut segmentation, followed by improved neck extraction based on intravascular ray-casting and robust closed-curve fit to the segmentation. Aneurysm segmentation and neck curve are used to compute three morphologic metrics: neck width, dome height and aspect ratio. The proposed framework was evaluated on ten cerebral 3D-DSA images containing saccular aneurysms. Sensitivity of aneurysm detection was 100% at 0.4 false positives per image. Compared to measurements of two expert raters, the values of metrics obtained by the proposed framework were accurate and, thus, suitable for assessing the risk of rupture.