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Deep learning for automated cerebral aneurysm detection on computed tomography images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and different machines to develop and evaluate an automatic detection model.

Methods

In this study, we have introduced a deep learning model, the faster RCNN model, in order to develop a tool for automatic detection of aneurysms from medical images. The inputs of the model were 2D nearby projection (NP) images from 3D CTA, which were made by the NP method proposed in this study. This method made aneurysms clearly visible on images and improved the model’s performance. The study included 311 patients with 352 aneurysms, selected from three hospitals, and 208 and 103 of these patients, respectively, were randomly selected to train and test the models.

Results

The sensitivity of the trained model was 91.8%. For aneurysm sizes larger than 3 mm, the sensitivity of successful aneurysm detection was 96.7%. We achieved state-of-the-art sensitivity for > 3 mm aneurysms. The sensitivities also indicated that there was no significant difference among aneurysms at different locations in the body. Computing time for the detection process was less than 25 s per case.

Conclusions

We successfully developed a deep learning model that can automatically detect aneurysms. The model performed well for aneurysms of different sizes or in different locations. This finding indicates that the deep learning model has the potential to vastly improve clinician performance by providing automated aneurysm detection.

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Acknowledgements

This study was supported by generous assistance from Dr Fengping Zhu, Department of Neurosurgery, Huashan Hospital, Fudan University.

Funding

No funding.

Author information

Author notes

  1. Xilei Dai and Lixiang Huang have contributed equally to this work.

Authors and Affiliations

  1. Faculty of Medicine and Health, Macquarie University, 75 Talavera Road, Sydney, Australia

    Xilei Dai, Yi Qian, Xiaoxi Hou & Chubin Ou

  2. Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China

    Xilei Dai & Junjie Liu

  3. Department of Radiology, Tianjin First Central Hospital, Tianjin, 300192, China

    Lixiang Huang & Shuang Xia

  4. Monash Imaging and Department of Surgery, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia

    Winston Chong

  5. Neurosurgery Unit, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia

    Antonio Di Ieva

  6. Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, Australia

    Yi Qian & Antonio Di Ieva

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  1. Xilei Dai
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Corresponding author

Correspondence to Yi Qian.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Dai, X., Huang, L., Qian, Y. et al. Deep learning for automated cerebral aneurysm detection on computed tomography images. Int J CARS 15, 715–723 (2020). https://doi.org/10.1007/s11548-020-02121-2

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  • DOI: https://doi.org/10.1007/s11548-020-02121-2

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