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Automatic ventricular nuclear magnetic resonance image processing with deep learning

Journal Article


Abstract


  • Cardiovascular diseases (CVD) seriously threaten the health of human beings, and they

    have caused widespread concern in recent years. At present, the diagnosis of CVD is

    mainly conducted by computed tomography (CT), echocardiography and nuclear magnetic

    resonance (NMR) technologies. NMR imaging technology is widely used in medical applications

    owing to its characteristics of high resolution and very low radiation. However,

    manual NMR image segmentation is time-consuming and error-prone, which has led to the

    research on automatic NMR image segmentation technologies. Researchers tend to explore

    the ventricular NRM image segmentation to improve the accuracy of CVD diagnosis. In

    this study, based on deep learning technology, we propose a layered Mask R-CNN segmentation

    method to segment ventricular NMR images. The experimental results show that the

    mean dice metrics (DM) of left ventricular segmentation and right ventricular segmentation

    are 0.92 and 0.89, and the Hausdorff distance (HD) metrics are 4.78 mm and 7.03 mm.

    Our research indicates that the proposed novel method has great potential to automate the

    ventricular NMR image segmentation. We also discuss the automatic abnormal ventricular

    systolic function detection method based on the proposed layered segmentation model.

UOW Authors


  •   Yong, Binbin (external author)
  •   Wang, Chen (external author)
  •   Shen, Jun
  •   Li, Fucun (external author)
  •   Yin, Hang (external author)

Publication Date


  • 2020

Citation


  • Yong, B., Wang, C., Shen, J., Li, F. & Yin, H. (2020). Automatic ventricular nuclear magnetic resonance image processing with deep learning. Multimedia Tools and Applications, Online first 1-17.

Scopus Eid


  • 2-s2.0-85084064536

Number Of Pages


  • 16

Start Page


  • 1

End Page


  • 17

Volume


  • Online first

Place Of Publication


  • United States

Abstract


  • Cardiovascular diseases (CVD) seriously threaten the health of human beings, and they

    have caused widespread concern in recent years. At present, the diagnosis of CVD is

    mainly conducted by computed tomography (CT), echocardiography and nuclear magnetic

    resonance (NMR) technologies. NMR imaging technology is widely used in medical applications

    owing to its characteristics of high resolution and very low radiation. However,

    manual NMR image segmentation is time-consuming and error-prone, which has led to the

    research on automatic NMR image segmentation technologies. Researchers tend to explore

    the ventricular NRM image segmentation to improve the accuracy of CVD diagnosis. In

    this study, based on deep learning technology, we propose a layered Mask R-CNN segmentation

    method to segment ventricular NMR images. The experimental results show that the

    mean dice metrics (DM) of left ventricular segmentation and right ventricular segmentation

    are 0.92 and 0.89, and the Hausdorff distance (HD) metrics are 4.78 mm and 7.03 mm.

    Our research indicates that the proposed novel method has great potential to automate the

    ventricular NMR image segmentation. We also discuss the automatic abnormal ventricular

    systolic function detection method based on the proposed layered segmentation model.

UOW Authors


  •   Yong, Binbin (external author)
  •   Wang, Chen (external author)
  •   Shen, Jun
  •   Li, Fucun (external author)
  •   Yin, Hang (external author)

Publication Date


  • 2020

Citation


  • Yong, B., Wang, C., Shen, J., Li, F. & Yin, H. (2020). Automatic ventricular nuclear magnetic resonance image processing with deep learning. Multimedia Tools and Applications, Online first 1-17.

Scopus Eid


  • 2-s2.0-85084064536

Number Of Pages


  • 16

Start Page


  • 1

End Page


  • 17

Volume


  • Online first

Place Of Publication


  • United States