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Laser & Optoelectronics Progress, Volume. 59, Issue 2, 0200005(2022)

Improved U-Net Models and Its Applications in Medical Image Segmentation: A Review

Huan Zhang, Dawei Qiu, Yibo Feng, and Jing Liu*
Author Affiliations
  • College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan , Shandong 250355, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (59)
    Cited By (6)
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    Figures & Tables(13)
    Schematic of U-Net architecture[6]

    Fig. 1. Schematic of U-Net architecture[6]

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    Schematic representation of MDU-Net architecture[8]

    Fig. 2. Schematic representation of MDU-Net architecture[8]

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    Schematic representation of R2U-Net architecture[13]

    Fig. 3. Schematic representation of R2U-Net architecture[13]

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    Schematic representation of Bridged U-Net[14]

    Fig. 4. Schematic representation of Bridged U-Net[14]

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    Schematic representation of M-Net architecture[15]

    Fig. 5. Schematic representation of M-Net architecture[15]

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    Summary of U-Net model improvement

    Fig. 6. Summary of U-Net model improvement

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    Normalization methods[38]

    Fig. 7. Normalization methods[38]

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    Partial blood vessel region segmentation diagram[45]. (a) Original color fundus retinal images; (b) local fundus retinal images; (c) local standard retinal segmentation images; (d) local retinal segmentation result images

    Fig. 8. Partial blood vessel region segmentation diagram[45]. (a) Original color fundus retinal images; (b) local fundus retinal images; (c) local standard retinal segmentation images; (d) local retinal segmentation result images

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    Three-dimensional segmentation results of different networks[51]

    Fig. 9. Three-dimensional segmentation results of different networks[51]

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    Segmentation results of Base U-Net and BSU-Net [2]

    Fig. 10. Segmentation results of Base U-Net and BSU-Net [2]

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    Segmentation results of brain tumor regions[58]

    Fig. 11. Segmentation results of brain tumor regions[58]

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    • Table 1. Improvement measures of U-Net network for different purposes

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      Table 1. Improvement measures of U-Net network for different purposes

      PurposeImprovement measureAssociated network
      Avoiding overfitting

      1）Multiple types of dense connections

      2）Multiple technologies of data enhancement

      		MDU-Net
      Reducing the number of parameters

      1）Inception module

      2）The global pooling layer

      3）Deep supervision

      4）Dense skip connection

      5）Full-scaled skip connection

      6）Skip connection using addition

      		MultiResUNet；BSU-Net；GP-UNet；DENSE-Inception U-Net；UNet++；UNet3+；LadderNet；Bridged U-Net
      Focusing on effective features and suppressing irrelevant features

      1）Attention module

      2）SE module

      		Attention U-Net；AnatomyNet；RA-UNet；ANU-Net
      Enhancing feature fusion

      1）Residual module

      2）Dense module

      3）Dense skip connection

      4）Full-scaled skip connection

      5）Feature pyramid

      6）Bidirectional feature network

      		Vnet；MDU-Net；FD-UNet；Bridged U-Net；Dense Multi-path U-Net；UNet++；UNet3+；RA-UNet；DPSN；U-Det
      Speeding up convergence

      1）Residual module

      2）Dense skip connection

      3）Full-scaled skip connection

      4）Bridged U-Net

      5）Data normalization

      VNet；GP-UNet；Bridged U-Net；

      UNet++；nnUNet

      Enlarging receptive field

      1）Deformable convolution

      2）Dilated convolution

      		DU-Net（Deformable U-Net）；DMFNet；BSU-Net；3D-HDC-Unet
      Avoiding gradient vanishing or gradient explosion

      1）Residual module

      2）Attention residual module

      		BSU-Net；RA-UNet；DENSE-Inception U-Net

    • Table 2. Summary of image segmentation for various diseases

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      Table 2. Summary of image segmentation for various diseases

      Area of segmentationMain image typeDifficultyImproved contentAssociated network
      Retinal vesselFundus color image

      1）The blood vessels are small

      2）Different shapes

      3）Accuracy of segmentation is low

      a）Adding deformable convolution or dilated convolution

      b）Adding up sampling feature channel

      c）Using attention mechanisms

      d）Adding inception module

      		DU-Net；CASU；
      Pulmonary noduleCT image

      1）The aim area of segmentation is small

      2）The edges are blurry

      3）The contrast is low

      4）The grayscale is uneven

      5）Similar to tissues such as blood vessels in the essence of the lungs

      6）Shape heterogeneity is high

      a）Expanding to 3D

      b）Dense module

      c）Deep supervision

      d）Multi-scaled feature extraction

      e）Attention mechanisms

      3D ResUNet；

      CRF 3D U-Net；

      MSVNet；

      Double attention 3D U-Net；

      Liver tumorCT image

      1）Shape and size are irregular

      2）Similar to surrounding organs

      3）There are differences in grayscale values

      a）Using conditional random field（CRF）

      b）Adding dense module or inception module

      c）Using inverted residual bottleneck block（IRB block）

      3D UNet-C2；

      BSU-Net；

      LV-Net；

      Brain tumorMRI image

      1）Shape heterogeneity is high

      2）The structure of brain tissue is complex

      3）The boundaries are blurry

      4）Class imbalance is prominent

      a）Multiple technologies of data enhancement

      b）Multiple network synthesis

      c）Mixed dilated convolution

      d）Mixed loss function

      3D U-Net；

      3D-HDC-UNet；

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    Huan Zhang, Dawei Qiu, Yibo Feng, Jing Liu. Improved U-Net Models and Its Applications in Medical Image Segmentation: A Review[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0200005

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    Paper Information

    Category: Reviews

    Received: Apr. 27, 2021

    Accepted: Jun. 27, 2021

    Published Online: Dec. 23, 2021

    The Author Email: Jing Liu (liuj_jn@163.com)

    DOI:10.3788/LOP202259.0200005

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology