  Predicting rupture risk and deciding on optimal treatment plan for
intracranial aneurysms (IAs) is possible by quantification of their size and
shape. For this purpose the IA has to be isolated from 3D angiogram.
State-of-the-art methods perform IA isolation by encoding neurosurgeon's
intuition about former non-dilated vessel anatomy through principled approaches
like fitting a cutting plane to vasculature surface, using Gaussian curvature
and vessel centerline distance constraints, by deformable contours or graph
cuts guided by the curvature or restricted by Voronoi surface decomposition and
similar. However, the large variability of IAs and their parent vasculature
configurations often leads to failure or non-intuitive isolation. Manual
corrections are thus required, but suffer from poor reproducibility. In this
paper, we aim to increase the accuracy, robustness and reproducibility of IA
isolation through two stage deep learning based segmentation of vascular
surface. The surface was represented by local patches in form of point clouds,
which were fed into first stage multilayer neural network (MNN) to obtain
descriptors invariant to point ordering, rotation and scale. Binary classifier
as second stage MNN was used to isolate surface belonging to the IA. Method
validation was based on 57 DSA, 28 CTA and 5 MRA images, where cross-validation
showed high segmentation sensitivity of 0.985, a substantial improvement over
0.830 obtained for the state-of-the-art method on the same datasets. Visual
analysis of IA isolation and its high accuracy and reliability consistent
across CTA and DSA scans confirmed the clinical applicability of proposed
method.
