Gottschalk T, Maier A, Kordon F, Kreher BW (2021)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2021
Publisher: Springer Vieweg
City/Town: Wiesbaden
Pages Range: 4-9
Conference Proceedings Title: Bildverarbeitung für die Medizin 2021
Event location: Virtual Conference
DOI: 10.1007/978-3-658-33198-6_4
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.
APA:
Gottschalk, T., Maier, A., Kordon, F., & Kreher, B.W. (2021). Learning-based Patch-wise Metal Segmentation with Consistency Check. In Palm C, Deserno TM, Handels H, Maier A, Maier-Hein K, Tolxdorff T (Eds.), Bildverarbeitung für die Medizin 2021 (pp. 4-9). Virtual Conference: Wiesbaden: Springer Vieweg.
MLA:
Gottschalk, Tristan, et al. "Learning-based Patch-wise Metal Segmentation with Consistency Check." Proceedings of the Bildverarbeitung für die Medizin 2021, Virtual Conference Ed. Palm C, Deserno TM, Handels H, Maier A, Maier-Hein K, Tolxdorff T, Wiesbaden: Springer Vieweg, 2021. 4-9.
BibTeX: Download