A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms

• URL: http://arxiv.org/abs/2006.16161v2
• Date: Sun, 4 Jul 2021 12:28:03 GMT
• Title: A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms
• Authors: Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi
• Abstract summary: We offer a two-step surface-based deep learning pipeline that achieves significantly higher performance. 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.
• Score: 18.163031102785904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%).

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