VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Abstract
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.
Cite
@article{arxiv.2001.09193,
title = {VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images},
author = {Anjany Sekuboyina and Malek E. Husseini and Amirhossein Bayat and Maximilian Löffler and Hans Liebl and Hongwei Li and Giles Tetteh and Jan Kukačka and Christian Payer and Darko Štern and Martin Urschler and Maodong Chen and Dalong Cheng and Nikolas Lessmann and Yujin Hu and Tianfu Wang and Dong Yang and Daguang Xu and Felix Ambellan and Tamaz Amiranashvili and Moritz Ehlke and Hans Lamecker and Sebastian Lehnert and Marilia Lirio and Nicolás Pérez de Olaguer and Heiko Ramm and Manish Sahu and Alexander Tack and Stefan Zachow and Tao Jiang and Xinjun Ma and Christoph Angerman and Xin Wang and Kevin Brown and Alexandre Kirszenberg and Élodie Puybareau and Di Chen and Yiwei Bai and Brandon H. Rapazzo and Timyoas Yeah and Amber Zhang and Shangliang Xu and Feng Hou and Zhiqiang He and Chan Zeng and Zheng Xiangshang and Xu Liming and Tucker J. Netherton and Raymond P. Mumme and Laurence E. Court and Zixun Huang and Chenhang He and Li-Wen Wang and Sai Ho Ling and Lê Duy Huynh and Nicolas Boutry and Roman Jakubicek and Jiri Chmelik and Supriti Mulay and Mohanasankar Sivaprakasam and Johannes C. Paetzold and Suprosanna Shit and Ivan Ezhov and Benedikt Wiestler and Ben Glocker and Alexander Valentinitsch and Markus Rempfler and Björn H. Menze and Jan S. Kirschke},
journal= {arXiv preprint arXiv:2001.09193},
year = {2022}
}
Comments
Challenge report for the VerSe 2019 and 2020. Published in Medical Image Analysis (DOI: https://doi.org/10.1016/j.media.2021.102166)