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With the advent of deep learning algorithms, fully automated radiologicalimage analysis is within reach. In spine imaging, several atlas- andshape-based as well as deep learning segmentation algorithms have beenproposed, allowing for subsequent automated analysis of morphology andpathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019)showed that these perform well on normal anatomy, but fail in variants notfrequently present in the training dataset. Building on that experience, wereport on the largely increased VerSe 2020 dataset and results from the seconditeration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020comprises annotated spine computed tomography (CT) images from 300 subjectswith 4142 fully visualized and annotated vertebrae, collected across multiplecentres from four different scanner manufacturers, enriched with cases thatexhibit anatomical variants such as enumeration abnormalities (n=77) andtransitional vertebrae (n=161). Metadata includes vertebral labellinginformation, voxel-level segmentation masks obtained with a human-machinehybrid algorithm and anatomical ratings, to enable the development andbenchmarking of robust and accurate segmentation algorithms.

A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data