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A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types.

Methods

We developed and evaluated “DeepWML”, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard).

Results

The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool’s performance increased with larger lesion volumes.

Conclusion

DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation.

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Data availability

Data that support the findings in this study are available from the corresponding author upon reasonable request.

Code availability

The source code generated during the current study is available from the corresponding author on reasonable request.

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Acknowledgements

Frederik Barkhof is supported by the NIHR biomedical research center at UCLH. The authors are grateful to Dr. Susumu Mori for his helpful suggestions.

Funding

This work was supported by National Natural Science Foundation of China (grant numbers: 81571631, 81870958), Beijing Nova Program (grant number: xx2013045) and Natural Science Foundation of Beijing Municipality (grant number: 7133244).

Author information

Author notes

  1. Yajing Zhang and Yunyun Duan contributed equally to the manuscript

Authors and Affiliations

  1. MR Clinical Science, Philips Healthcare, 258 Zhongyuan Rd, Suzhou, SIP, China

    Yajing Zhang & Xiaoyang Wang

  2. Department of Radiology, Beijing Tiantan Hospital, Fengtai District, Capital Medical University, No. 119 the West Southern 4th Ring Road, Beijing, 100070, China

    Yunyun Duan, Zhizheng Zhuo & Yaou Liu

  3. Department of Imaging and Medical Informatics, University of Geneva, Geneva, Switzerland

    Sven Haller

  4. Faculty of Medicine, University of Geneva, Geneva, Switzerland

    Sven Haller

  5. Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands

    Frederik Barkhof

  6. Queen Square Institute of Neurology and Center for Medical Image Computing, University College, London, UK

    Frederik Barkhof

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  1. Yajing Zhang
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  2. Yunyun Duan
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  4. Zhizheng Zhuo
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  7. Yaou Liu
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Contributions

All authors contributed to the study conception and design. Data collection, methodology and data analysis were performed by Yajing Zhang, Yunyun Duan and Xiaoyang Wang. The first draft of the manuscript was written by Yajing Zhang and Yaou Liu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yaou Liu.

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Conflict of interest

The authors declare no competing interests.

Ethics approval

This study was approved by the institutional review boards of Beijing Tiantan Hospital, Capital Medical University, Beijing, China. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Consent to participate

Written informed consent was obtained from each participant.

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Consent for publication was obtained from all participants.

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Cite this article

Zhang, Y., Duan, Y., Wang, X. et al. A deep learning algorithm for white matter hyperintensity lesion detection and segmentation. Neuroradiology 64, 727–734 (2022). https://doi.org/10.1007/s00234-021-02820-w

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  • DOI: https://doi.org/10.1007/s00234-021-02820-w

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