International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China; Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China.
International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China.
J Struct Biol. 2023 Sep;215(3):107991. doi: 10.1016/j.jsb.2023.107991. Epub 2023 Jul 13.
Cell recognition methods are in high demand in cell biology and medicine, and the method based on atomic force microscopy (AFM) shows a great value in application. The difference in mechanical properties or morphology of cells has been frequently used to detect whether cells are cancerous, but this detection method cannot be a general means for cancer cell detection, and the traditional artificial feature extraction method also has its limitations. In this work, we proposed an analytic method based on the physical properties of cells and deep learning method for recognizing cell types. The residual neural network used for recognition was modified by multi-scale convolutional fusion, attention mechanism and depthwise separable convolution, so as to optimize feature extraction and reduce operation costs. In the method, the collected cells were imaged by AFM, and the processed images were analyzed by the optimized convolutional neural network. The recognition results of two groups of cells (HL-7702 and SMMC-7721, SGC-7901 and GES-1) by this method show that the recognition rate of dataset with the combination of cell surface morphology, adhesion and Young's modulus is higher, and the recognition rate of the dataset with optimal resolution is higher. Our study indicated that the recognition of physical properties of cells using deep learning technology can serve as a universal and effective method for the automated analysis of cell information.
细胞识别方法在细胞生物学和医学领域有很高的需求，基于原子力显微镜（AFM）的方法在应用中显示出很大的价值。细胞的机械性能或形态的差异已被频繁用于检测细胞是否癌变，但这种检测方法不能作为癌细胞检测的通用手段，传统的人工特征提取方法也存在局限性。在这项工作中，我们提出了一种基于细胞物理特性和深度学习方法的细胞类型识别分析方法。用于识别的残差神经网络通过多尺度卷积融合、注意力机制和深度可分离卷积进行了修改，以优化特征提取并降低操作成本。在该方法中，通过 AFM 对采集的细胞进行成像，然后通过优化的卷积神经网络对处理后的图像进行分析。该方法对两组细胞（HL-7702 和 SMMC-7721、SGC-7901 和 GES-1）的识别结果表明，结合细胞表面形态、黏附和杨氏模量的数据集的识别率更高，而具有最佳分辨率的数据集的识别率更高。我们的研究表明，使用深度学习技术识别细胞的物理特性可以作为细胞信息自动分析的通用且有效的方法。
