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  • Open Access


    CenterPicker: An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

    Jianquan Ouyang1,*, Jinling Wang1, Yaowu Wang1, Tianming Liu2

    Journal of Cyber Security, Vol.4, No.2, pp. 65-77, 2022, DOI:10.32604/jcs.2022.028065

    Abstract Cryo-electron microscopy (cryo-EM) has become one of the mainstream techniques for determining the structures of proteins and macromolecular complexes, with prospects for development and significance. Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction. However, existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio (SNR) of micrographs is extremely low, thereby directly affecting the efficiency and accuracy of 3D reconstruction. We propose an automated particle-picking method (CenterPicker) based on particle center point detection to… More >

  • Open Access


    A Noise Extraction Method for Cryo-EM Single-Particle Denoising

    Huanrong Tang1, Sihan Wang1, Jianquan Ouyang1,*, Tianming Liu2

    Journal on Big Data, Vol.4, No.1, pp. 61-76, 2022, DOI:10.32604/jbd.2022.028078

    Abstract Cryo-Electron Microscopy (cryo-EM) has become a powerful method to study the structure and function of biological macromolecules. However, in clustering tasks based on the projection angle of particles in cryo-EM, the noise considerably affects the clustering results. Existing denoising algorithms are ineffective due to the extremely low signal-to-noise ratio (SNR) of cryo-EM images and the complexity of noise types. The noise of a single particle greatly influences the orientation estimation of the subsequent clustering task, and the result of the clustering task directly affects the accuracy of the 3D reconstruction. In this paper, we propose… More >

  • Open Access


    Urdnet: A Cryo-EM Particle Automatic Picking Method

    Jianquan Ouyang1, Yue Zhang1, Kun Fang1,2,*, Tianming Liu3, Xiangyu Pan2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1593-1610, 2022, DOI:10.32604/cmc.2022.025072

    Abstract Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and More >

  • Open Access


    Research on Denoising of Cryo-em Images Based on Deep Learning

    Jianquan Ouyang*, Yi He, Huanrong Tang, Zhousong Fu

    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 1-9, 2020, DOI:10.32604/jihpp.2020.010657

    Abstract Cryo-em (Cryogenic electron microscopy) is a technology this can build bio-macromolecule of three-dimensional structure. Under the condition of now, the projection image of the biological macromolecule which is collected by the Cryo-em technology that the contrast is low, the signal to noise is low, image blurring, and not easy to distinguish single particle from background, the corresponding processing technology is lagging behind. Therefore, make Cryoem image denoising useful, and maintaining bio-macromolecule of contour or signal of function-construct improve Cryo-em image quality or resolution of Cryo-em three-dimensional structure have important effect. This paper researched a denoising… More >

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