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Authors: Christian Payer 1 ; Darko Štern 1 ; Horst Bischof 2 and Martin Urschler 3

Affiliations: 1 Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria, Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria ; 2 Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria ; 3 Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria, School of Computer Science, The University of Auckland, Auckland, New Zealand

Keyword(s): Vertebrae Localization, Vertebrae Segmentation, SpatialConfiguration-Net, U-Net, VerSe 2019 Challenge.

Abstract: Localization and segmentation of vertebral bodies from spine CT volumes are crucial for pathological diagnosis, surgical planning, and postoperative assessment. However, fully automatic analysis of spine CT volumes is difficult due to the anatomical variation of pathologies, noise caused by screws and implants, and the large range of different field-of-views. We propose a fully automatic coarse to fine approach for vertebrae localization and segmentation based on fully convolutional CNNs. In a three-step approach, at first, a U-Net localizes the rough position of the spine. Then, the SpatialConfiguration-Net performs vertebrae localization and identification using heatmap regression. Finally, a U-Net performs binary segmentation of each identified vertebrae in a high resolution, before merging the individual predictions into the resulting multi-label vertebrae segmentation. The evaluation shows top performance of our approach, ranking first place and winning the MICCAI 2019 Large Sca le Vertebrae Segmentation Challenge (VerSe 2019). (More)

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Paper citation in several formats:
Payer, C.; Štern, D.; Bischof, H. and Urschler, M. (2020). Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 124-133. DOI: 10.5220/0008975201240133

@conference{visapp20,
author={Christian Payer. and Darko Štern. and Horst Bischof. and Martin Urschler.},
title={Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={124-133},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008975201240133},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net
SN - 978-989-758-402-2
IS - 2184-4321
AU - Payer, C.
AU - Štern, D.
AU - Bischof, H.
AU - Urschler, M.
PY - 2020
SP - 124
EP - 133
DO - 10.5220/0008975201240133

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