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Original research
Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network
  1. Hailan Jin1,
  2. Jiewen Geng2,3,
  3. Yin Yin1,
  4. Minghui Hu1,
  5. Guangming Yang1,
  6. Sishi Xiang2,3,
  7. Xiaodong Zhai2,3,
  8. Zhe Ji2,3,
  9. Xinxin Fan4,
  10. Peng Hu2,3,
  11. Chuan He2,3,
  12. Lan Qin1,
  13. Hongqi Zhang2,3
  1. 1 Department of R&D, UnionStrong (Beijing) Technology Co.Ltd, Beijing, China
  2. 2 China International Neuroscience Institute (China-INI), Beijing, China
  3. 3 Department of Neurosurgery, Xuanwu Hospital,Capital Medical University, Beijing, China
  4. 4 Department of Neurosurgery, Xi'an NO.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, Shanxi Province, China
  1. Correspondence to Dr Hongqi Zhang, Department of Neurosurgery, Xuanwu Hospital, Beijing 100176, China; xwzhanghq{at}; Dr Lan Qin; qinlan{at}


Background Intracranial aneurysms (IAs) are common in the population and may cause death.

Objective To develop a new fully automated detection and segmentation deep neural network based framework to assist neurologists in evaluating and contouring intracranial aneurysms from 2D+time digital subtraction angiography (DSA) sequences during diagnosis.

Methods The network structure is based on a general U-shaped design for medical image segmentation and detection. The network includes a fully convolutional technique to detect aneurysms in high-resolution DSA frames. In addition, a bidirectional convolutional long short-term memory module is introduced at each level of the network to capture the change in contrast medium flow across the 2D DSA frames. The resulting network incorporates both spatial and temporal information from DSA sequences and can be trained end-to-end. Furthermore, deep supervision was implemented to help the network converge. The proposed network structure was trained with 2269 DSA sequences from 347 patients with IAs. After that, the system was evaluated on a blind test set with 947 DSA sequences from 146 patients.

Results Of the 354 aneurysms, 316 (89.3%) were successfully detected, corresponding to a patient level sensitivity of 97.7% at an average false positive number of 3.77 per sequence. The system runs for less than one second per sequence with an average dice coefficient score of 0.533.

Conclusions This deep neural network assists in successfully detecting and segmenting aneurysms from 2D DSA sequences, and can be used in clinical practice.

  • aneurysm
  • angiography
  • technique

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  • HJ and JG contributed equally.

  • Contributors HJ, JG, and HZ conceived and designed the research. HJ, JG, YY, MH, GY, SX, XZ, ZJ, and XF collected and reviewed the data. HJ and JG analyzed the data and performed the statistical analysis. PH, CH, HZ, and LQ handled funding and supervision. HJ and JG drafted the manuscript. All authors made critical revisions of the manuscript and reviewed the final version.

  • Funding This work was supported by the National Key Research Development Program grant number 2016YFC1300800 and National Natural Science Foundation of China grant number 81500988.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval This study was approved by our institutional ethics committee (Xuanwu Hospital, No.2017082).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request. Because of the sensitive nature of the data, it is available upon request to the corresponding author.