Urdnet: A Cryo-EM Particle Automatic Picking Method

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 highresolution 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 and automatically locate particles from cryo-electron microscopy images, and solve the bottleneck that cryo-EM Single-particle biological macromolecule reconstruction requires tens of thousands of automatically picked particles. The 80S ribosome, HCN1 channel and TcdA1 toxin subunits, and other public protein datasets have been trained and tested on Urdnet. The experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION, DeepPicker, and acquire the 3D structure of picked particles with higher resolution.

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method

“…Compared with convolutional networks, it achieves high accuracy in image classification. In 2022, our research team proposed a residual dense neural network Urdnet [ 11 ] model based on U-Net, which obtains high-resolution information during upsampling to achieve precise segmentation and positioning. It fills in the underlying information through skip connections to improve segmentation accuracy and has good advantages in cryo-EM image biological image processing.…”