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Acta Optica Sinica, Volume. 40, Issue 17, 1710001(2020)

Cell Image Segmentation Method Based on Residual Block and Attention Mechanism

Wenxiu Zhang1,2,3、*, Zhencai Zhu1,2,3, Yonghe Zhang1,2,3, Xinyu Wang1,2, and Guopeng Ding1,2
Author Affiliations
  • 1Innovation Academy for Microsatellite, Chinese Academy of Sciences, Shanghai 201203, China
  • 2Key Laboratory of Microsatellites, Chinese Academy of Sciences, Shanghai 201203, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (23)
    Cited By (16)
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    Figures & Tables(11)
    Overall framework of proposed model

    Fig. 1. Overall framework of proposed model

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    Structure of residual block

    Fig. 2. Structure of residual block

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    Structure of attention mechanism

    Fig. 3. Structure of attention mechanism

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    Display of dataset. (a) Training set image; (b) corresponding ground truth

    Fig. 4. Display of dataset. (a) Training set image; (b) corresponding ground truth

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    Local image blocks and corresponding ground truth

    Fig. 5. Local image blocks and corresponding ground truth

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    Cell segmentation results of group 1. (a) Origin image; (b) ground truth; (c) Otsu model; (d) FCN-8s model; (e) U-Net model; (f) SegNet model; (g) R2U-Net model; (h) proposed model

    Fig. 6. Cell segmentation results of group 1. (a) Origin image; (b) ground truth; (c) Otsu model; (d) FCN-8s model; (e) U-Net model; (f) SegNet model; (g) R2U-Net model; (h) proposed model

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    Cell segmentation results of group 2. (a) Origin image; (b) ground truth; (c) Otsu model; (d) FCN-8s model; (e) U-Net model; (f) SegNet model; (g) R2U-Net model; (h) proposed model

    Fig. 7. Cell segmentation results of group 2. (a) Origin image; (b) ground truth; (c) Otsu model; (d) FCN-8s model; (e) U-Net model; (f) SegNet model; (g) R2U-Net model; (h) proposed model

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    Cell segmentation results of group 3. (a) Origin image; (b) ground truth; (c) Otsu model; (d) FCN-8s model; (e) U-Net model; (f) SegNet model; (g) R2U-Net model; (h) proposed model

    Fig. 8. Cell segmentation results of group 3. (a) Origin image; (b) ground truth; (c) Otsu model; (d) FCN-8s model; (e) U-Net model; (f) SegNet model; (g) R2U-Net model; (h) proposed model

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    Detail comparison of segmentation results. (a) Ground truth; (b) FCN-8s model; (c) U-Net model; (d) SegNet model; (e) R2U-Net model; (f) proposed model

    Fig. 9. Detail comparison of segmentation results. (a) Ground truth; (b) FCN-8s model; (c) U-Net model; (d) SegNet model; (e) R2U-Net model; (f) proposed model

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    • Table 1. Quantitative analysis of different segmentation models

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      Table 1. Quantitative analysis of different segmentation models

      ModelPixel AccuracyIoUDice Score
      Otsu0.79060.23470.3778
      FCN-8s0.90080.66720.7964
      U-Net0.93490.74050.8504
      SegNet0.93360.73590.8516
      R2U-Net0.94350.76360.8691
      Proposed model0.94470.78220.8775

    • Table 2. Influence of each module on whole model

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      Table 2. Influence of each module on whole model

      U-NetResidual blockAttention mechanismPixel AccuracyIoUDice Score
      +0.93470.74820.8592
      ++0.93950.75050.8603
      ++0.93880.75650.8652
      +++0.94630.77570.8776
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    Wenxiu Zhang, Zhencai Zhu, Yonghe Zhang, Xinyu Wang, Guopeng Ding. Cell Image Segmentation Method Based on Residual Block and Attention Mechanism[J]. Acta Optica Sinica, 2020, 40(17): 1710001

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    Paper Information

    Category: Image Processing

    Received: Mar. 6, 2020

    Accepted: May. 29, 2020

    Published Online: Aug. 24, 2020

    The Author Email: Zhang Wenxiu (zwxy@mail.ustc.edu.cn)

    DOI:10.3788/AOS202040.1710001

    Topics

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