Amin Khosrozadeh,Raphaela Seeger,Guillaume Witz,Julika Radecke,Jakob B Sørensen,Benoît Zuber
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引用次数: 0
Abstract
Cryo-electron tomography (cryo-ET) has the potential to reveal cell structure down to atomic resolution. Nevertheless, cellular cryo-ET data is highly complex, requiring image segmentation for visualization and quantification of subcellular structures. Due to noise and anisotropic resolution in cryo-ET data, automatic segmentation based on classical computer vision approaches usually does not perform satisfactorily. Communication between neurons relies on neurotransmitter-filled synaptic vesicle (SV) exocytosis. Cryo-ET study of the spatial organization of SVs and their interconnections allows a better understanding of the mechanisms of exocytosis regulation. Accurate SV segmentation is a prerequisite to obtaining a faithful connectivity representation. Hundreds of SVs are present in a synapse, and their manual segmentation is a bottleneck. We addressed this by designing a workflow consisting of a convolutional network followed by post-processing steps. Alongside, we provide an interactive tool for accurately segmenting spherical vesicles. Our pipeline can in principle segment spherical vesicles in any cell type as well as extracellular and in vitro spherical vesicles.
期刊介绍:
The Journal of Cell Biology (JCB) is a comprehensive journal dedicated to publishing original discoveries across all realms of cell biology. We invite papers presenting novel cellular or molecular advancements in various domains of basic cell biology, along with applied cell biology research in diverse systems such as immunology, neurobiology, metabolism, virology, developmental biology, and plant biology. We enthusiastically welcome submissions showcasing significant findings of interest to cell biologists, irrespective of the experimental approach.