VerSe: A Vertebrae Labelling and Segmentation Benchmark

Anjany Sekuboyina, Amirhossein Bayat, Malek E Husseini, Maximilian Löffler, Markus Rempfler, Jan Kukačka, Giles Tetteh, Alexander Valentinitsch, Christian Payer, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Stefan Zachowk, Tao JiangXinjun Ma, Christoph Angerman, Xin Wang, Qingyue Wei, Kevin Brown, Matthias Wolf, Alexandre Kirszenberg, Élodie Puybareauq, Björn H. Menze, Jan S Kirschke

Research output: Working paperPreprint


In this paper we report the challenge set-up and results of the Large Scale Vertebrae Segmentation Challenge (VerSe) organized in conjunction with the MICCAI 2019. The challenge consisted of two tasks, vertebrae labelling and vertebrae segmentation. For this a total of 160 multidetector CT scan cohort closely resembling clinical setting was prepared and was annotated at a voxel-level by a human-machine hybrid algorithm. In this paper we also present the annotation protocol and the algorithm that aided the medical experts in the annotation process. Eleven fully automated algorithms were benchmarked on this data with the best performing algorithm achieving a vertebrae identification rate of 95% and a Dice coefficient of 90%. VerSe'19 is an open-call challenge at its image data along with the annotations and evaluation tools will continue to be publicly accessible through its online portal.
Original languageEnglish
Publication statusPublished - 24 Jan 2020

Publication series e-Print archive
PublisherCornell University Library

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