Abstract
Cryo electron tomography (cryo-ET) is an important tool to obtain macromolecular complex assembly and in situ macromolecular structure. The purpose of cryo-ET processing is to obtain the position of particles of interest from the reconstructed tomogram through particle picking, and then classify and average them to obtain a high-resolution three-dimensional structure. Particle picking is critical to obtaining high-resolution structures. Therefore, we focuses on the characteristics of cryo-ET image. The combination of region proposals and classifiers are used to achieve object detection tasks on special images with large size, low signal-to-noise ratio, and low contrast. We redesigned class labels based on granular and non-granular features in the image. Active learning strategies were used to achieve effective particle picking. In the experimental part of this article, a double-output resnet3d network was designed as a classifier based on an active learning strategy for the cryo-ET image dataset of ribosomes of saccharomyces cerevisiae, and achieved good results in particle picking. The accuracy rate can reach the above 81.9%.
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Mo, M., Kong, F., Liu, Q. (2021). Particle Picking Method for Cryo Electron Tomography Image Based on Active Learning. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_40
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DOI: https://doi.org/10.1007/978-3-030-87571-8_40
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