Nanoparticle Segmentation Based on U-Net Convolutional Neural Network

Fang Zhang; Yue Wu; Zhitao Xiao; Lei Geng; Jun Wu; Yanbei Liu; Wen Wang

doi:10.3788/LOP56.061005

Laser & Optoelectronics Progress, Volume. 56, Issue 6, 061005(2019)

Nanoparticle Segmentation Based on U-Net Convolutional Neural Network

Fang Zhang^1,2, Yue Wu¹, Zhitao Xiao^1,2、*, Lei Geng^1,2, Jun Wu^1,2, Yanbei Liu^1,2, and Wen Wang^1,2

Author Affiliations

¹ School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China

² Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin 300387, China

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Figures & Tables(9)

Fig. 1. Spherical nanoparticle image taken by TEM and its partially enlarged view. (a) TEM image; (b) partially enlarged view

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Fig. 2. Filtering results by partial differential equation. (a) Original nanoparticle image; (b) filtering result

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Fig. 3. Network structure with BN

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Fig. 4. Comparison chart of loss curves

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Fig. 5. Comparison chart 1 of segmentation effect. (a) Level set algorithm; (b) PixelNet; (c) original U-Net; (d) improved network

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Fig. 6. Comparison chart 2 of segmentation effect. (a) Level set algorithm; (b) PixelNet; (c) original U-Net; (d) improved network

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Fig. 7. Comparison chart 3 of segmentation effect. (a) Level set algorithm; (b) PixelNet; (c) original U-Net; (d) improved network

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Table 1. Objective indexes of testing results
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Table 1. Objective indexes of testing results
Method Number ofsamples MeanR_IOU MeanR_ACC
Level set method 3470 0.8562 0.8775
PixelNet 3470 0.9187 0.9405
Original U-Net 3470 0.9222 0.9472
Improved U-Net 3470 0.9402 0.9630

Table 2. Comparison of average time consumption
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Table 2. Comparison of average time consumption
Method Levelsetmethod PixelNet OriginalU-Net ImprovedU-Net
Timeconsumption /ms 438 315 276 288

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Fang Zhang, Yue Wu, Zhitao Xiao, Lei Geng, Jun Wu, Yanbei Liu, Wen Wang. Nanoparticle Segmentation Based on U-Net Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061005

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