VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images

•A total of 374 multi-detector CT scans are made available to the research community, the biggest such dataset on spine until date (VerSe’19: https://osf.io/nqjyw/; VerSe’20: https://osf.io/t98fz/).•The VerSe benchmark includes annotations for two fundamental processing tasks, namely vertebrae labelling and segmentation.•Twenty-six fully-automated algorithms (eleven for VerSe’19, thirteen for VerSe’20, and one baseline) are benchmarked on this dataset, with the top performing algorithm achieving a mean vertebrae identification rate of 96.6% and a Dice coefficient of 91.7% in VerSe’20.•Further insights into these algorithms are provided by examining them at various levels of granularity ranging from dataset-level experiments to vertebrae-level performances to a field-of-view-related analysis.