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A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework.

Methods

A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework.

Results

Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts.

Conclusion

The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods.

Clinical relevance statement

Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification.

Key Points

•There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms.

•The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images.

•The deep learning framework is robust and has the potential to be applied in a clinical setting.

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Abbreviations

3D CNN:

Three-dimensional convolutional neural network

CCC:

Concordance correlation coefficient

CLAHE:

Contrast limitation adaptive histogram equalization

CTA:

Computer tomography angiography

DSA:

Digital subtraction angiography

FOV:

Field of view

FPs:

False positives

IAs:

Intracranial aneurysms

MAE:

Mean absolute error

MBC:

Minimum bounding cube

MRA:

Magnetic resonance angiography

ROIs:

Regions of interest

SAH:

Subarachnoid hemorrhage

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Acknowledgements

This manuscript has a pre-print: http://dx.doi.org/10.2139/ssrn.4174298.

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 62171300, 82171290), Natural Science Foundation of Beijing Municipality (Nos. 7222050, L192013), and Beijing Municipal Administration of Hospital’s Ascent Plan (DFL20190501).

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Capital Medical University, Beijing, China

    Junda Qu, Yutang Li, Ting Chen, Xu Zhang & Chunlin Li

  2. Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China

    Junda Qu, Yutang Li, Ting Chen, Xu Zhang & Chunlin Li

  3. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China

    Junda Qu, Yutang Li, Ting Chen, Xu Zhang & Chunlin Li

  4. Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

    Hao Niu, Fei Peng, Jiaxiang Xia, Xiaoxin He, Boya Xu, Xuge Chen & Aihua Liu

  5. Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Hao Niu, Fei Peng, Jiaxiang Xia, Xiaoxin He, Boya Xu, Xuge Chen & Aihua Liu

  6. Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China

    Rui Li

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  1. Junda Qu
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Corresponding authors

Correspondence to Aihua Liu, Xu Zhang or Chunlin Li.

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Guarantor

The scientific guarantor of this publication is professor Xu Zhang, PhD, from Capital Medical University (zhangxu@ccmu.edu.cn).

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Informed consent

The institutional review board waived the requirement to obtain informed patient consent for this retrospective study.

Ethical approval

This study was approved by the Institutional Ethical Committee of Beijing Tiantan Hospital.

Study subjects or cohorts overlap

No.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Junda Qu and Hao Niu contributed equally to this work.

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Qu, J., Niu, H., Li, Y. et al. A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10295-x

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