Optoelectronics Letters, Volume. 18, Issue 6, 378(2022)

An improved U-Net for cell confluence estimation

Hua BAI1... Changhao LU1, Ming MA1,*, Shulin YAN2,3, Jianzhong ZHANG2,3, and Zhibo HAN2,34 |Show fewer author(s)
Author Affiliations
  • 1Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
  • 2Tianjin Key Laboratory of Engineering Technologies for Cell Phamaceutical, Tianjin 300457, China
  • 3National Engineering Research Center of Cell Products/AmCellGene Co., Ltd., Tianjin 300457, China
  • 4State Key Lab of Experimental Hematology, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
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    Cell confluence is an important metric to determine the growth and the best harvest time of adherent cells. At present, the evaluation of cell confluence mainly relies on experienced labor, and thus it is not conducive to the automated cell culture. In this paper, we proposed an improved U-Net algorithm (called DU-Net) for the segmentation of adherent cells. First, the general convolution was replaced by the dilated convolution to expand the receptive fields for feature extraction. Then, the convolutional layers were combined with the batch normalization layers to reduce the dependence of the network on initialization. As a result, the segmentation accuracy and F1-score of the proposed DU-Net for adherent cells with low confluence (<50%) reached 96.94% and 93.87%, respectively, and for those with high confluence (≥50%), they reached 98.63% and 98.98%, respectively. Further, the paired t-test results showed that the proposed DU-Net was statistically superior to the traditional U-Net algorithm.

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    BAI Hua, LU Changhao, MA Ming, YAN Shulin, ZHANG Jianzhong, HAN Zhibo. An improved U-Net for cell confluence estimation[J]. Optoelectronics Letters, 2022, 18(6): 378

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    Paper Information

    Received: Aug. 22, 2021

    Accepted: Feb. 23, 2022

    Published Online: Jan. 20, 2023

    The Author Email: Ming MA (maming@tiangong.edu.cn)

    DOI:10.1007/s11801-022-1129-3

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