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.
