VerSe: A Vertebrae Labelling and Segmentation Benchmark

A. Sekuboyina, A. Bayat, M. Husseini, M. Loffler, M. Rempfler, J. Kukacka, G. Tetteh, A. Valentinitsch, C. Payer, D. Stern, M. Urschler, M. Chen, D. Cheng, N. Lessmann, Y. Hu, T. Wang, D. Yang, D. Xu, F. Ambellan, T. Amiranashvili, M. Ehlke, H. Lamecker, S. Lehnert, M. Lirio, N. de Olaguer, H. Ramm, M. Sahu, A. Tack, S. Zachow, T. Jiang, X. Ma, C. Angerman, X. Wang, Q. Wei, K. Brown, M. Wolf, A. Kirszenberg, E. Puybareauq, B. Menze and J. Kirschke

arXiv:2001.09193 2020.

arXiv Cited by ~2

This work is a technical report concerning the Large Scale Vertebrae Segmentation Challenge (VerSe) organised in conjunction with the MICCAI 2019. The challenge set-up consisting of two tasks, vertebrae labelling and vertebrae segmentation, is detailed. A total of 160 multidetector CT scans closely resembling a typical spine-centreed clinical setting were prepared and annotated at voxel-level by a human-machine hybrid algorithm. Both the annotation protocol and the algorithm that aided the medical experts in this annotation process are presented. More importantly, eleven fully automated algorithms of the participating teams were submitted to be benchmarked on the VerSe data. This work presents a detailed performance analysis of these algorithms 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 and its image data along with the annotations and evaluation tools will continue to be publicly accessible through its online portal.