Jianquan Ouyang^1,*, 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 automatically select a large number… More >

Jianquan Ouyang¹, Yue Zhang¹, Kun Fang^1,2,*, 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 pixel-level tags, used to accurately… More >