Feature detection in cryo-electron tomography image analysis
Uddin, Mostofa Rafid Dip, Sajib Acharjee Jha, Rajat Aayush Zeng, Xiangrui Xu, Min
Uddin, Mostofa Rafid
Dip, Sajib Acharjee
Jha, Rajat Aayush
Zeng, Xiangrui
Xu, Min
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Department
Computer Vision
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Book chapter
Date
2025
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English
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Abstract
Cryo-electron tomography (cryo-ET) is an emerging technology that provides in situ visualization of subcellular structures in subnanometer resolution. With functionalities of both cellular and structural images, cryo-ET images provide a rich amount of features regarding organelles, macromolecular structures, and their spatial organization. However, due to several imaging artifacts, the cryo-ET images are extremely noisy and contain missing data upon reconstruction. Consequently, detecting the rich amount of features from cryo-ET images is difficult and requires an extensive level of computational processing. In this chapter, we delve into the computational methods, their use cases, and comparative advantages and disadvantages for detecting diverse types of features from cryo-ET images.
Citation
M. R. Uddin, S. A. Dip, R. A. Jha, X. Zeng, and M. Xu, “Feature detection in cryo-electron tomography image analysis,” Cryo-electron Tomography: A Journey From Sample Preparation to Data Mining, pp. 173–215, Jan. 2025, doi: 10.1016/B978-0-443-18829-9.00008-6
Source
Cryo-electron Tomography: A Journey from Sample Preparation to Data Mining
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Elsevier