Cross-Domain Metal Segmentation for CBCT Metal Artifact Reduction

Rohleder M, Gottschalk T, Maier A, Kreher BW (2022)

Publication Type: Conference contribution

Publication year: 2022


Publisher: SPIE

Book Volume: 12304

Conference Proceedings Title: Proceedings of SPIE - The International Society for Optical Engineering

Event location: Online

ISBN: 9781510656697

DOI: 10.1117/12.2646382


Metallic objects in the volume of a CBCT system can cause various artifacts after image reconstruction such as bright and dark streaks, local distortions of CT values and shadowing. In the intraoperative setting, this drastically reduces clinical value and can harden decision making. Most existing approaches to reduce such artifacts rely on a threshold-based metal segmentation in the reconstruction domain, which is prone to failure; especially in cases with extreme artifacts. Faulty metal masks impair these inpainting-based MAR methods and at times even worsen image quality by introducing secondary artifacts. In this work, a novel neural network topology is proposed to segment metallic objects in CBCT reconstruction domain by leveraging information of the given raw projection images. A reconstruction operator is embedded into this architecture, which enables the model to yield projection and reconstruction domain information during end-to-end training. This cross-domain approach is compared to the self-configuring segmentation method”nnUNet”, which predicts the three dimensional metal masks directly from the artifact corrupted reconstruction. To provide a baseline, a segmentation using a global dice-optimal threshold is determined. Segmentation results on simulated data confirmed by 5-fold cross validation show that the cross-domain network yields a mean dice coefficient of 0.87 ± 0.05 at a prediction time of 4s per volume. The reference method achieves 0.86 ± 0.03 in 43s, whereas the optimal similarity using a threshold averages to 0.45 ± 0.22.

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How to cite


Rohleder, M., Gottschalk, T., Maier, A., & Kreher, B.W. (2022). Cross-Domain Metal Segmentation for CBCT Metal Artifact Reduction. In Joseph Webster Stayman (Eds.), Proceedings of SPIE - The International Society for Optical Engineering. Online: SPIE.


Rohleder, Maximilian, et al. "Cross-Domain Metal Segmentation for CBCT Metal Artifact Reduction." Proceedings of the 7th International Conference on Image Formation in X-Ray Computed Tomography, Online Ed. Joseph Webster Stayman, SPIE, 2022.

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