AITom: AI-guided cryo-electron tomography image analyses toolkit
Zhan, Xueying Zeng, Xiangrui Uddin, Mostofa Rafid Xu, Min
Zhan, Xueying
Zeng, Xiangrui
Uddin, Mostofa Rafid
Xu, Min
Supervisor
Department
Computer Vision
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Type
Journal article
Date
2025
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Language
English
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Abstract
Cryo-electron tomography (cryo-ET) is an essential tool in structural biology, uniquely capable of visualizing three-dimensional macromolecular complexes within their native cellular environments, thereby providing profound molecular-level insights. Despite its significant promise, cryo-ET faces persistent challenges in the systematic localization, identification, segmentation, and structural recovery of three-dimensional subcellular components, necessitating the development of efficient and accurate large-scale image analysis methods. In response to these complexities, this paper introduces AITom, an open-source artificial intelligence platform tailored for cryo-ET researchers. AITom integrates a comprehensive suite of public and proprietary algorithms, supporting both traditional template-based and template-free approaches, alongside state-of-the-art deep learning methodologies for cryo-ET data analysis. By incorporating diverse computational strategies, AITom enables researchers to more effectively tackle the complexities inherent in cryo-ET, facilitating precise analysis and interpretation of complex biological structures. Furthermore, AITom provides extensive tutorials for each analysis module, offering valuable guidance to users in utilizing its comprehensive functionalities.
Citation
X. Zhan, X. Zeng, M. R. Uddin, and M. Xu, “AITom: AI-guided cryo-electron tomography image analyses toolkit,” J Struct Biol, vol. 217, no. 2, p. 108207, Jun. 2025, doi: 10.1016/J.JSB.2025.108207.
Source
Journal of Structural Biology
Conference
Keywords
Cryo-electron tomography, Computer vision, Machine learning, Image segmentation, Image classification
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Source
Publisher
Elsevier