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

arXiv: https://arxiv.org/abs/2006.16161 · HTML: https://mlsanity.com/paper/2006.16161/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction](https://mlsanity.com/paper/2210.09309/) (2022)
- [3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark](https://mlsanity.com/paper/2310.08904/) (2023)
- [Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structures](https://mlsanity.com/paper/2404.14435/) (2024)

## Abstract (arXiv)

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%).