International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Centre for Opto/Bio-Nano Measurement and Manufacturing, Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528437, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China; College of Physics, Changchun University of Science and Technology, Changchun 130022, China.
International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Centre for Opto/Bio-Nano Measurement and Manufacturing, Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528437, 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. 2024 Sep;216(3):108107. doi: 10.1016/j.jsb.2024.108107. Epub 2024 Jun 19.
Atomic force microscope enables ultra-precision imaging of living cells. However, atomic force microscope imaging is a complex and time-consuming process. The obtained images of living cells usually have low resolution and are easily influenced by noise leading to unsatisfactory imaging quality, obstructing the research and analysis based on cell images. Herein, an adaptive attention image reconstruction network based on residual encoder-decoder was proposed, through the combination of deep learning technology and atomic force microscope imaging supporting high-quality cell image acquisition. Compared with other learning-based methods, the proposed network showed higher peak signal-to-noise ratio, higher structural similarity and better image reconstruction performances. In addition, the cell images reconstructed by each method were used for cell recognition, and the cell images reconstructed by the proposed network had the highest cell recognition rate. The proposed network has brought insights into the atomic force microscope-based imaging of living cells and cell image reconstruction, which is of great significance in biological and medical research.
原子力显微镜能够实现对活细胞的超精密成像。然而，原子力显微镜成像过程复杂且耗时。活细胞的获得图像通常分辨率较低，容易受到噪声的影响，导致成像质量不佳，阻碍了基于细胞图像的研究和分析。在此，提出了一种基于残差编解码器的自适应注意图像重建网络，通过将深度学习技术与原子力显微镜成像相结合，支持高质量的细胞图像采集。与其他基于学习的方法相比，所提出的网络具有更高的峰值信噪比、更高的结构相似性和更好的图像重建性能。此外，使用每种方法重建的细胞图像进行细胞识别，所提出的网络重建的细胞图像具有最高的细胞识别率。该网络为基于原子力显微镜的活细胞成像和细胞图像重建提供了新的思路，在生物和医学研究中具有重要意义。
