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Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images

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Abstract

The accuracy of computed tomography angiography (CTA) image interpretation depends on the radiologist. This study aims to develop a new method for automatically detecting intracranial aneurysms from CTA images using deep learning, based on a convolutional neural network (CNN) implemented on the DeepMedic platform. Ninety CTA scans of patients with intracranial aneurysms are collected and divided into two datasets: training (80 subjects) and test (10 subjects) datasets. Subsequently, a deep learning architecture with a three-dimensional (3D) CNN model is implemented on the DeepMedic platform for the automatic segmentation and detection of intracranial aneurysms from the CTA images. The samples in the training dataset are used to train the CNN model, and those in the test dataset are used to assess the performance of the established system. Sensitivity, positive predictive value (PPV), and false positives are evaluated. The overall sensitivity and PPV of this system for detecting intracranial aneurysms from CTA images are 92.3% and 100%, respectively, and the segmentation sensitivity is 92.3%. The performance of the system in the detection of intracranial aneurysms is closely related to their size. The detection sensitivity for small intracranial aneurysms (≤ 3 mm) is 66.7%, whereas the sensitivity of detection for large (> 10 mm) and medium-sized (3–10 mm) intracranial aneurysms is 100%. The deep learning architecture with a 3D CNN model on the DeepMedic platform can reliably segment and detect intracranial aneurysms from CTA images with high sensitivity.

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Availability of Data and Materials

Data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This study was supported in part by the National Natural Science Foundation of China (81601558), Breeding Foundation of Zhuhai People’s Hospital of China (2019PY-16), and the Medical Research Foundation of Zhuhai City of China (20191207A010017).

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Author notes

  1. Xiujuan Liu and Jun Mao contributed equally and are joint co-first authors.

Authors and Affiliations

  1. Department of Radiology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China

    Xiujuan Liu, Jun Mao, Xiangrong Yu, Ye Tian, Jianming Wang, Jianchao Liang & Ligong Lu

  2. Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing, 210000, China

    Ning Sun & Lei Chai

  3. Department of Neurosurgery, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated With Jinan University) Zhuhai, Guangdong, 519000, China

    Haiquan Tao & Kaihua Wu

  4. Department of Radiology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210000, Jiangsu, China

    Lihua Yuan, Jiaming Lu, Yang Wang & Bing Zhang

  5. CT Room, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China

    Yiding Wang & Mengjiao Chen

  6. Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, NY, 10032, USA

    Zhishun Wang

Authors
  1. Xiujuan Liu
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  2. Jun Mao
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  17. Zhishun Wang
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  18. Ligong Lu
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Contributions

XL and JM: literature search, study design, data collection, data processing and experimentation, data analysis and interpretation, and manuscript writing; NS and LC: data processing and experimentation; XY, YT, JW, and JL: data collection and manual labeling of CTA images of People’s Hospital (Zhuhai Hospital Affiliated with Jinan University); HT and KW: data collection of DSA and surgery; LY, JL, YW, and BZ: data analysis and interpretation; YW and MC: data collection and manual labeling of CTA images of the First Affiliated Hospital of Harbin Medical University; and ZW and LL: study design, data analysis and interpretation, and manuscript writing.

Corresponding authors

Correspondence to Zhishun Wang or Ligong Lu.

Ethics declarations

Ethics Approval

This retrospective study was approved by the Ethics Committee of Zhuhai People’s Hospital and the First Affiliated Hospital of Harbin Medical University. Informed consent was obtained from all participants.

Consent to Participate

Verbal informed consent was obtained prior to the interview.

Consent for Publication

Participants have consented to the submission of the case report to the journal.

Conflict of Interest

The authors declare no competing interests.

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Liu, X., Mao, J., Sun, N. et al. Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images. J Digit Imaging 36, 114–123 (2023). https://doi.org/10.1007/s10278-022-00698-5

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  • DOI: https://doi.org/10.1007/s10278-022-00698-5

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