Acta Photonica Sinica, Volume. 52, Issue 8, 0817002(2023)

Magnetic Resonance Imaging Brain Tumor Segmentation Using Multiscale Ghost Generative Adversarial Network

Muqing ZHANG1...2, Yutong HAN1,2, Bonian CHEN1,2, and Jianxin ZHANG12,* |Show fewer author(s)
Author Affiliations
  • 1School of Computer Science and Engineering,Dalian Minzu University,Dalian 116600,China
  • 2Institute of Machine Intelligence and Biocomputing,Dalian Minzu University,Dalian 116600,China
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    Figures & Tables(10)
    Overall architecture of multiscale ghost generate adversarial network for MRI brain tumor segmentation
    Structural of the ghost module
    Structure of the multiscale generator
    Structure of the discriminator
    MRI images in different modes and ground truth
    Visualization segmentation results on the BraTS 2020 training dataset
    • Table 1. Ablation experiments on BraTS2020 training dataset

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      Table 1. Ablation experiments on BraTS2020 training dataset

      MethodsDSC
      ETWTTCAverage
      U-Net(baseline)0.7840.9000.7970.827
      GU-Net0.8260.9150.8270.856
      GU-Net+Ghost0.8210.9210.8350.859
      GU-Net+Mul0.8240.9200.8460.863
      MG2AN(ours)0.8250.9220.8540.867
    • Table 2. Ablation experiments on BraTS2020 validation dataset

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      Table 2. Ablation experiments on BraTS2020 validation dataset

      MethodsDSC
      ETWTTCAverage
      U-Net(baseline)0.7690.8940.7990.821
      GU-Net0.7710.8990.8030.824
      GU-Net+Ghost0.7740.9010.8090.828
      GU-Net+Mul0.7740.9010.8210.832
      MG2AN(ours)0.7820.9030.8260.837
    • Table 3. Compared results with representative methods on BraTS2019 validation set

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      Table 3. Compared results with representative methods on BraTS2019 validation set

      MethodsDSCHausdorff95
      ETWTTCAverageETWTTCAverage
      Ref.[110.7070.8780.7790.788----
      Ref.[140.7090.8730.8140.79912.3015.4512.4713.40
      Ref.[150.7890.9000.8190.8363.735.646.055.14
      Ref.[170.7670.8970.7900.8184.616.928.406.64
      Ref.[230.7520.8990.8150.82212.567.398.069.34
      MG2AN0.7700.9020.8360.8363.747.065.555.45
    • Table 4. Compared results with representative methods on BraTS2020 validation set

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      Table 4. Compared results with representative methods on BraTS2020 validation set

      MethodsDSCHausdorff95
      ETWTTCAverageETWTTCAverage
      Ref.[120.7600.9000.8000.82026.85.2512.414.82
      Ref.[130.7630.8990.8160.82633.265.287.7415.43
      Ref.[150.7870.9010.8170.83517.954.969.7710.89
      Ref.[180.7870.8720.8110.82324.366.4418.9516.58
      Ref.[230.7640.8990.8100.82432.567.3912.0617.33
      MG2AN0.7820.9030.8260.83729.414.548.9113.43
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    Muqing ZHANG, Yutong HAN, Bonian CHEN, Jianxin ZHANG. Magnetic Resonance Imaging Brain Tumor Segmentation Using Multiscale Ghost Generative Adversarial Network[J]. Acta Photonica Sinica, 2023, 52(8): 0817002

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    Paper Information

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    Received: Feb. 15, 2023

    Accepted: Apr. 4, 2023

    Published Online: Sep. 26, 2023

    The Author Email: ZHANG Jianxin (jxzhang0411@163.com)

    DOI:10.3788/gzxb20235208.0817002

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