Vesicle3D: An Integrative Platform for 3D Segmentation and Analysis of Vesicles in Cryo-electron Tomograms

Synaptic vesicles (SVs) are essential components of neurotransmission, and their structure and organization are critical determinants of synaptic function. Cryo-electron tomography (cryo-ET) enables the in situ visualization of SVs in a near-native state. However, their accurate segmentation remains challenging owing to the low signal-to-noise ratio, missing-wedge artifacts in synaptic tomograms, and the inherent morphological heterogeneity of SVs. Herein, we present Vesicle3D, an integrated platform that combines: (1) a vesicle-specific denoising and missing-wedge restoration model; (2) a dual-pathway 3D Res-UNet with prediction fusion; (3) ellipsoid-aware post-processing to preserve authentic vesicle morphology; and (4) an interactive, Napari-based graphical user interface for correction and fine-tuning. This method outperformed existing methods, particularly in the detection of ellipsoidal SVs, and demonstrated robust generalization across diverse datasets, including chemically fixed neurons, isolated synaptosomes, and cryo-FIB lamellae. Furthermore, fine-tuning with approximately 200 annotations enabled rapid adaptation to new datasets. Vesicle3D provides a scalable and versatile framework for the large-scale quantitative analysis of vesicles in cryo-tomograms.