VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Creators: name: Sekuboyina, Anjany
name: Husseini, Malek E.
name: Bayat, Amirhossein
name: Löffler, Maximilian
name: Liebl, Hans
name: Li, Hongwei
name: Tetteh, Giles
name: Kukačka, Jan
name: Payer, Christian
name: Štern, Darko
name: Urschler, Martin
name: Chen, Maodong
name: Cheng, Dalong
name: Lessmann, Nikolas
name: Hu, Yujin
name: Wang, Tianfu
name: Yang, Dong
name: Xu, Daguang
name: Ambellan, Felix
name: Amiranashvili, Tamaz
name: Ehlke, Moritz
name: Lamecker, Hans
name: Lehnert, Sebastian
name: Lirio, Marilia
name: de Olaguer, Nicolás Pérez
name: Ramm, Heiko
name: Sahu, Manish
name: Tack, Alexander
name: Zachow, Stefan
name: Jiang, Tao
name: Ma, Xinjun
name: Angerman, Christoph
name: Wang, Xin
name: Brown, Kevin
name: Kirszenberg, Alexandre
name: Puybareau, Élodie
name: Chen, Di
name: Bai, Yiwei
name: Rapazzo, Brandon H.
name: Yeah, Timyoas
name: Zhang, Amber
name: Xu, Shangliang
name: Hou, Feng
name: He, Zhiqiang
name: Zeng, Chan
name: Xiangshang, Zheng
name: Liming, Xu
name: Netherton, Tucker J.
name: Mumme, Raymond P.
name: Court, Laurence E.
name: Huang, Zixun
name: He, Chenhang
name: Wang, Li-Wen
name: Ling, Sai Ho
name: Huynh, Lê Duy
name: Boutry, Nicolas
name: Jakubicek, Roman
name: Chmelik, Jiri
name: Mulay, Supriti
name: Sivaprakasam, Mohanasankar
name: Paetzold, Johannes C.
name: Shit, Suprosanna
name: Ezhov, Ivan
name: Wiestler, Benedikt
name: Glocker, Ben
name: Valentinitsch, Alexander
name: Rempfler, Markus
name: Menze, Björn H.
name: Kirschke, Jan S.
Publisher: name: arXiv
Year: 2020
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Image and Video Processing (eess.IV)
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: 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.
description: Challenge report for the VerSe 2019 and 2020. Published in Medical Image Analysis (DOI: https://doi.org/10.1016/j.media.2021.102166)