  The exact shape of intracranial aneurysms is critical in medical diagnosis
and surgical planning. While voxel-based deep learning frameworks have been
proposed for this segmentation task, their performance remains limited. In this
study, we offer a two-step surface-based deep learning pipeline that achieves
significantly higher performance. Our proposed model takes a surface model of
entire principal brain arteries containing aneurysms as input and returns
aneurysms surfaces as output. A user first generates a surface model by
manually specifying multiple thresholds for time-of-flight magnetic resonance
angiography images. The system then samples small surface fragments from the
entire brain arteries and classifies the surface fragments according to whether
aneurysms are present using a point-based deep learning network (PointNet++).
Finally, the system applies surface segmentation (SO-Net) to surface fragments
containing aneurysms. We conduct a direct comparison of segmentation
performance by counting voxels between the proposed surface-based framework and
the existing voxel-based method, in which our framework achieves a much higher
dice similarity coefficient score (72%) than the prior approach (46%).
