Publication detail

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

SEKUBOYINA, A. HUSSEINI, M. BAYAT, A. et al.

Original Title

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

Type

journal article in Web of Science

Language

English

Original 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 of 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 tackling the 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 results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and 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 VerSe content and code can be accessed at: https://github.com/anjany/verse.

Keywords

Spine; Vertebrae; Segmentation; Labelling

Authors

SEKUBOYINA, A.; HUSSEINI, M.; BAYAT, A.; et al.

Released

22. 7. 2021

Publisher

Elsevier B.V.

Location

Amsterdam, The Netherlands

ISBN

1361-8415

Periodical

MEDICAL IMAGE ANALYSIS

Year of study

73

Number

4

State

Kingdom of the Netherlands

Pages from

1

Pages to

32

Pages count

32

URL

https://www.sciencedirect.com/science/article/pii/S1361841521002127

BibTex

@article{BUT172091,
  author="Anjany {Sekuboyina} and Malek El {Husseini} and Amirhossein {Bayat} and Maximilian T. {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 {Huỳnh} and Nicolas {Boutry} and Roman {Jakubíček} and Jiří {Chmelík} 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}",
  title="VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images",
  journal="MEDICAL IMAGE ANALYSIS",
  year="2021",
  volume="73",
  number="4",
  pages="1--32",
  doi="10.1016/j.media.2021.102166",
  issn="1361-8415",
  url="https://www.sciencedirect.com/science/article/pii/S1361841521002127"
}