A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms
This work addresses the problem of accurate aneurysm segmentation for medical diagnosis and surgical planning, representing an incremental improvement over existing methods.
The authors tackled the segmentation of intracranial aneurysms from brain artery surfaces, proposing a two-step surface-based deep learning pipeline that achieved a dice similarity coefficient of 72%, significantly outperforming a prior voxel-based method at 46%.
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%).