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.

中文翻译：

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CryoVesNet：冷冻电子断层扫描中突触小泡分割的专用框架。


冷冻电子断层扫描 （cryo-ET） 有可能揭示低至原子分辨率的细胞结构。然而，细胞冷冻电子断层扫描数据非常复杂，需要图像分割来可视化和量化亚细胞结构。由于冷冻电子断层扫描数据中的噪声和各向异性分辨率，基于经典计算机视觉方法的自动分割通常效果不佳。神经元之间的通信依赖于神经递质填充的突触小泡 （SV） 胞吐作用。对 SV 的空间组织及其相互联系的冷冻电子断层扫描研究可以更好地了解胞吐调节的机制。准确的 SV 分段是获得忠实的连接表示的先决条件。突触中存在数百个 SV，它们的手动分割是一个瓶颈。我们通过设计一个由卷积网络和后处理步骤组成的工作流来解决这个问题。此外，我们还提供了一个交互式工具，用于准确分割球形囊泡。我们的管道原则上可以分割任何细胞类型的球形囊泡以及细胞外和体外球形囊泡。