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Unsupervised deep learning of bright-field images for apoptotic cell classification

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Abstract

The classification of apoptotic and living cells is significant in drug screening and treating various diseases. Conventional supervised methods require a large amount of prelabelled data, which are often costly and consume immense human resources in the biological field. In this study, unsupervised deep learning algorithms were used to extract cell characteristics and classify cells. A model integrating a convolutional neural network and an autoencoder network was utilised to extract cell characteristics, and a hybrid clustering approach was employed to obtain cell feature clustering results. Experiments on both public and private datasets revealed that the proposed unsupervised strategy performs well in cell categorisation. For instance, in the public dataset, our method obtained a precision of 96.72% on only 1000 unlabelled cells. To the best of our knowledge, this is the first time unsupervised deep learning has been applied to distinguish apoptosis and live cells with high accuracy.

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Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by grants from the Key-Area Research and Development Program of Guangdong Province (Grant Number: 2022B0303040003) and the National Natural Science Foundation of China (NSFC) (Grant Numbers: 62135003 and 61875056).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China

    Zhuo Zheng, Beini Sun, Siqi He, Guanchen Wang, Chenyang Bi & Tongsheng Chen

  2. SCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., South China Normal University, Qingyuan, 511500, China

    Tongsheng Chen

Authors
  1. Zhuo Zheng
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  2. Beini Sun
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  3. Siqi He
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  4. Guanchen Wang
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  5. Chenyang Bi
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  6. Tongsheng Chen
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Contributions

Z-Z designed and performed experiments, analysed data and wrote the paper. BN-S provided cell pictures. SQ-H, GC-W and CY-B find relevant information and provide ideas. TS-C designed the study and planned the experiments.

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Correspondence to Tongsheng Chen.

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Zheng, Z., Sun, B., He, S. et al. Unsupervised deep learning of bright-field images for apoptotic cell classification. SIViP 17, 3657–3664 (2023). https://doi.org/10.1007/s11760-023-02592-1

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  • DOI: https://doi.org/10.1007/s11760-023-02592-1

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