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Deep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images

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Abstract

White matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual or semi-automatic delineation and intuitive localization, which is time-consuming and observer-dependent. Current automatic quantification methods focus mainly on the segmentation, but the spatial distribution of lesions plays a vital role in clinical diagnosis. In this study, we implemented four segmentation algorithms and compared the performances quantitatively and qualitatively on two open-access datasets. The location-specific analysis was conducted sequentially on 213 clinical patients with cerebral ischemia and lacune. The experimental results suggest that our deep-learning-based model has the potential to be integrated into the clinical workflow.

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Acknowledgements

This manuscript has been accepted for presentation in The Fourth CCF Bioinformatics Conference (CBC 2019).

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Author notes

  1. Wenhao Jiang and Fengyu Lin contributed equally to the work.

Authors and Affiliations

  1. YIWEI Medical Technology Co., Ltd, Room 1001, MAI KE LONG Building, Nanshan, ShenZhen, 518000, China

    Wenhao Jiang, Fengyu Lin, Jian Zhang, Taowei Zhan & Silun Wang

  2. Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, SAR, China

    Wenhao Jiang & Peng Cao

  3. School of Computer Science, Harbin Institute of Technology, Harbin, 150001, China

    Wenhao Jiang

Authors
  1. Wenhao Jiang
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  2. Fengyu Lin
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  3. Jian Zhang
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  4. Taowei Zhan
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  5. Peng Cao
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  6. Silun Wang
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Correspondence to Silun Wang.

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Jiang, W., Lin, F., Zhang, J. et al. Deep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images. Interdiscip Sci Comput Life Sci 12, 438–446 (2020). https://doi.org/10.1007/s12539-020-00398-0

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  • DOI: https://doi.org/10.1007/s12539-020-00398-0

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