Abstract
Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions. Metal Artifact Reduction (MAR) methods, whose first step is always a segmentation of the present metal objects, try to remove these artifacts. Thereby, the segmentation is a crucial task which has strong influence on the MAR's outcome. This study proposes and evaluates a learning-based patch-wise segmentation network and a newly proposed Consistency Check as post-processing step. The combination of the learned segmentation and Consistency Check reaches a high segmentation performance with an average IoU score of 0.924 on the test set. Furthermore, the Consistency Check proves the ability to significantly reduce false positive segmentations whilst simultaneously ensuring consistent segmentations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stille M, Kratz B, Müller J, et al. Influence of metal segmentation on the quality of metal artifact reduction methods. In: Medical Imaging 2013: Physics of Medical Imaging. vol. 8668. International Society for Optics and Photonics. SPIE; 2013. p. 902–907.
Meyer E, Raupach R, Lell M, et al. Normalized metal artifact reduction (NMAR) in computed tomography. Med Phys. 2010;37(10):5482–5493.
Meyer E, Raupach R, Lell M, et al. Frequency split metal artifact reduction (FSMAR) in computed tomography. Med Phys. 2012;39(4):1904–1916.
Ronneberger O, Fischer P, Brox T; Springer. U-net: Convolutional networks for biomedical image segmentation. Proc MICCAI. 2015; p. 234–241.
Yu L, Zhang Z, Li X, et al. Deep sinogram completion with image prior for metal artifact reduction in CT images. IEEE Trans Med Imaging. 2020; p. 1–1.
Peng C, Li B, Li M, et al. An irregular metal trace inpainting network for X-ray CT metal artifact reduction. Med Phys. 2020;47(9):4087{4100. Available from: https://aapm.onlinelibrary.wiley.com/doi/abs/https://doi.org/10.1002/mp.14295.
Gottschalk TM, Kreher BW, Kunze H, et al. Deep learning based metal inpainting in the projection domain: Initial results. In: Proc MLMIR. Springer; 2019. p. 125–136.
Maier A, Steidl S, Christlein V, et al. Medical imaging systems: An introductory guide. vol. 11111. Springer; 2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Gottschalk, T.M., Maier, A., Kordon, F., Kreher, B.W. (2021). Learning-based Patch-wise Metal Segmentation with Consistency Check. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-658-33198-6_4
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-33197-9
Online ISBN: 978-3-658-33198-6
eBook Packages: Computer Science and Engineering (German Language)