作者
Anjany Sekuboyina, Amirhossein Bayat, Malek E Husseini, Maximilian Löffler, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Qingyue Wei, Kevin Brown, Matthias Wolf, Alexandre Kirszenberg, Élodie Puybareau, Alexander Valentinitsch, Markus Rempfler, Björn H Menze, Jan S Kirschke
发表日期
2021/10
期刊
Medical Image Analysis
卷号
73
页码范围
102166
出版商
Elsevier
简介
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 …
学术搜索中的文章
A Sekuboyina, ME Husseini, A Bayat, M Löffler, H Liebl… - Medical image analysis, 2021
A Sekuboyina, A Bayat, ME Husseini, M Löffler… - 2020