AITom: Open-source AI platform for cryo-electron tomography data analysis
This addresses the need for efficient and accurate large-scale image analysis methods for cryo-ET researchers, though it is incremental as it integrates existing algorithms into a new platform.
The authors tackled the challenge of analyzing cryo-electron tomography data by introducing AITom, an open-source AI platform that provides algorithms for localization, identification, segmentation, and structural recovery of subcellular components, resulting in a tool that supports both traditional and deep learning approaches with remote interactive capabilities.
Cryo-electron tomography (cryo-ET) is an emerging technology for the 3D visualization of structural organizations and interactions of subcellular components at near-native state and sub-molecular resolution. Tomograms captured by cryo-ET contain heterogeneous structures representing the complex and dynamic subcellular environment. Since the structures are not purified or fluorescently labeled, the spatial organization and interaction between both the known and unknown structures can be studied in their native environment. The rapid advances of cryo-electron tomography (cryo-ET) have generated abundant 3D cellular imaging data. However, the systematic localization, identification, segmentation, and structural recovery of the subcellular components require efficient and accurate large-scale image analysis methods. We introduce AITom, an open-source artificial intelligence platform for cryo-ET researchers. AITom provides many public as well as in-house algorithms for performing cryo-ET data analysis through both the traditional template-based or template-free approach and the deep learning approach. AITom also supports remote interactive analysis. Comprehensive tutorials for each analysis module are provided to guide the user through. We welcome researchers and developers to join this collaborative open-source software development project. Availability: https://github.com/xulabs/aitom