Ting Da
ABSTRACT
A novel method for particle picking in cryo-electron microscopy (cryo-EM) based on a convolutional neural network (CNN) is proposed. The key to successful 3D reconstruction lies in the ability to pick as many particles as possible before 2D class averaging. In most of the existing studies, particles are selected either manually or semi-automatically, which can be time-consuming and laborious. We aim to pick particles fully automatically to improve the picking efficiency without any human intervention. A new CNN model is designed and two data preprocessing methods, image sharpening and histogram equalization, are employed to make the model get better performance. The experimental results show that the proposed method has a better recall score compared to existing algorithms. Moreover, the proposed model is validated and compared using various EM data. With the fully automatically picked particles, 2D class averaging can be processed efficiently to further select good-quality particles. Subsequently, 3D reconstruction can be performed.
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Index Terms
- A Method for Fully Automated Particle Picking in Cryo-Electron Microscopy Based on a CNN
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