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Computational Visual Media 2023, 9(1): 57-69 https://doi.org/10.1007/s41095-022-0270-z
Research Article | Open Access | Issue | Published: 18 October 2022

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

Show Author's Information Xi Yang1,2( ),  Ding Xia1,  Taichi Kin1,  Takeo Igarashi1
The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo 113-8654, Japan
Jilin University, No. 2699, Qianjin Road, Changchun, China
Keywords:
intracranial aneurysm (IA) segmentation, point-based 3D deep learning, medical image segmentation
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Yang X, Xia D, Kin T, et al. A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms. Computational Visual Media, 2023, 9(1): 57-69. https://doi.org/10.1007/s41095-022-0270-z
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Abstract Full text Electronic supplementary material About this article

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 better results. Our proposed model takes a surface model of an entire set of principal brain arteries containing aneurysms as input and returns aneurysm 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 set of 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 the segmentation performance of our proposed surface-based framework and an existing voxel-based method by counting voxels: our framework achieves a much higher Dice similarity ( 72%) than the prior approach ( 46%).


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About this article

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

Show Author's information Xi Yang1,2( ), Ding Xia1, Taichi Kin1, Takeo Igarashi1
The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo 113-8654, Japan
Jilin University, No. 2699, Qianjin Road, Changchun, China

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 better results. Our proposed model takes a surface model of an entire set of principal brain arteries containing aneurysms as input and returns aneurysm 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 set of 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 the segmentation performance of our proposed surface-based framework and an existing voxel-based method by counting voxels: our framework achieves a much higher Dice similarity ( 72%) than the prior approach ( 46%).

Keywords: intracranial aneurysm (IA) segmentation, point-based 3D deep learning, medical image segmentation

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Received: 08 July 2021
Accepted: 14 January 2022
Published: 18 October 2022
Issue date: March 2023

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© The Author(s) 2022.

Acknowledgements

This research was supported by AMED under Grant No. JP18he1602001.

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