Vertebral labelling and segmentation are two fundamental tasks in an
automated spine processing pipeline. Reliable and accurate processing of spine
images is expected to benefit clinical decision-support systems for diagnosis,
surgery planning, and population-based analysis on spine and bone health.
However, designing automated algorithms for spine processing is challenging
predominantly due to considerable variations in anatomy and acquisition
protocols and due to a severe shortage of publicly available data. Addressing
these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was
organised in conjunction with the International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a
call for algorithms towards labelling and segmentation of vertebrae. Two
datasets containing a total of 374 multi-detector CT scans from 355 patients
were prepared and 4505 vertebrae have individually been annotated at
voxel-level by a human-machine hybrid algorithm (https://osf.io/nqjyw/,
https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these
datasets. In this work, we present the the results of this evaluation and
further investigate the performance-variation at vertebra-level, scan-level,
and at different fields-of-view. We also evaluate the generalisability of the
approaches to an implicit domain shift in data by evaluating the top performing
algorithms of one challenge iteration on data from the other iteration. The
principal takeaway from VerSe: the performance of an algorithm in labelling and
segmenting a spine scan hinges on its ability to correctly identify vertebrae
in cases of rare anatomical variations. The content and code concerning VerSe
can be accessed at: https://github.com/anjany/verse.