Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (12): 68-74.doi: 10.16180/j.cnki.issn1007-7820.2021.12.012

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Semantic Segmentation of Cervical Cell Image Based on Weak Supervision

ZHANG Can,CHEN Wei,YIN Zhong   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology, Shanghai 200093,China
  • Received:2020-08-09 Online:2021-12-15 Published:2021-12-06
  • Supported by:
    National Natural Science Foundation of China(61703277)


Neural network algorithm based on weakly supervised label is a hot research topic in medical field. In view of the lack of labeling data and the inaccurate segmentation of cytoplasm and nucleus, this study proposes a semantic segmentation algorithm based on weakly supervised cervical cell images. First, the algorithm uses unsupervised K-means as the labeling function to generate cell image segmentation labels. Then, training is conducted through an improved Encoder-Decoder network. Subsequently, CRF is introduced as the last layer of the network to integrate the global information of the image and optimize the segmentation results. The label and prediction images are optimized and trained in three times to achieve pixel level classification of cell images. Finally, the algorithm is verified using the cervical cell image dataset. The experimental results show that the algorithm has high generalization ability, and the accuracy rate is up to 96.7%.

Key words: weak supervision, K-means, semantic segmentation, convolutional neural network, cervical cell, Encoder-Decoder, CRF, optimization training

CLC Number: 

  • TP391