Computer Science ›› 2020, Vol. 47 ›› Issue (8): 213-220.doi: 10.11896/jsjkx.190600026

Special Issue: Medical Imaging

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GNNI U-net:Precise Segmentation Neural Network of Left Ventricular Contours for MRI Images Based on Group Normalization and Nearest Interpolation

GAO Qiang1, GAO Jing-yang1, ZHAO Di2   

  1. 1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
    2 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:GAO Qiang, born in 1994, postgra-duate, is a member of China Computer Federation.His main research interests include deep learning, medical image analysis.
    GAO Jing-yang, born in 1966, professor, Ph.D supervisor, is a member of China Computer Federation.Her main research interests include medical image analysis based on deep learning, big data analysis of genomics based on machine learning.
  • Supported by:
    This work was supported by the Natural Science Foundation of Beijing, China(5182018), National Basic Research Program of China(SQ2017ZX106047), Natural Science Foundation of Beijing, China(4161004) and Beijing Science and Technology Project(Z171100000117001, Z161100000216143).

Abstract: Cardiovascular disease has become the No.1 killer of human health.At present, doctors manually label the left ventricle contour by left ventricular MRI imaging technology to calculate various functional parameters of the heart to prevent cardiovascular disease, which is not difficult but time consuming and cumbersome.Deep learning has achieved remarkable success in many areas of medical image segmentation, but there is still room for improvement in the field of left ventricular contour segmentation.We proposes a convolutional neural network based on group normalization and nearest interpolation up-sampling which is called GNNI U-net (U-net with Group normalization and Nearest interpolation) for MRI left ventricular contour precise segmentation.We constructe a convolution module with group normalization operation based on group normalization method for fast and accurately feature extraction.Based on nearest neighbor interpolation method, we constructe an up-sampling module for feature restoring.We conducte a pre-processing method for Center cropping ROI extraction and a detailed controlled experiment of GNNI U-net on the Sunnybrook left ventricular segmentation dataset and the LVSC left ventricular segmentation dataset.The experimental results show that the GNNI U-net obtains the accuracy of Dice coefficient of 0.937 and the accuracy of Jaccard coefficient of 0.893 on the Sunnybrook dataset, and it obtains the accuracy of the Dice coefficient of 0.957 and the accuracy of the Jaccard coefficient of 0.921 on the LVSC dataset.The GNNI U-net network achieves higher Dice coefficient accuracy than other convolutional network segmentation methods in the field of left ventricular contour segmentation.Finally, according to the experimental results, we discusse and verifie that the convolutional module of the normalization operation can accelerate the convergence of the network and improve the accuracy, and the up-sampling module using the nearest neighbor interpolation method is more friendly to the smaller target segmentation such as the left ventricle contour, and our model can accelerate network convergence to a certain extent.

Key words: GNNI U-net, Group normalization, Left ventricle segmentation, Nearest interpolation

CLC Number: 

  • TP391.7
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