Gas vesicles, micrometer-scale protein structures that function as bacterial buoyancy providers, encapsulate gas in a highly optimized manner. While their atomic structure has been elucidated through single-particle analysis of cryo-EM images, certain structural and functional details remain uncertain. Its biogenesis - the formation and growth mechanisms - consequently remains elusive. Here we apply automated segmentation methods originating from cell imaging to cryo-EM images of gas vesicles to analyze the positions of gas vesicle features in a context-preserving matter. Subsequent whole gas vesicle processing is able to transform accurate gas vesicle segmentations into high-confidence structural feature location picks and statistics. This enables the formation of a sizeable data set containing 86k whole gas vesicles, improved resolution 2D class averages, and the potential for improved structural modeling. Combining high sample number contextual information enables inference on the dynamical properties of gas vesicle growth. Our findings validate recent atomic structure propositions and lend support to a stochastic monomer insertion growth model.