Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising
Zweng M, Fallavollita P, Demirci S, Kowarschik M, Navab N, Mateus D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9365
Pages Range: 114-123
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319246000
DOI: 10.1007/978-3-319-24601-7_12
Abstract
In neuro-interventional surgeries, physicians rely on fluoroscopic video sequences to guide tools through the vascular system to the region of interest. Due to the low signal-to-noise ratio of low-dose images and the presence of many line-like structures in the brain, the guide-wire and other tools are difficult to see. In this work we propose an effective method to detect guide-wires in fluoroscopic videos that aims at enhancing the visualization for better intervention guidance. In contrast to prior work, we do not rely on a specific modeling of the catheter (e.g. shape, intensity, etc.), nor on prior statistical learning. Instead, we base our approach on motion cues by making use of recent advances in low-rank and sparse matrix decomposition, which we then combine with denoising. An evaluation on 651 X-ray images from 5 patient shows that our guide-wire tip detection is precise and within clinical tolerance for guide-wire inter-frame motions as high as 6 mm.
Involved external institutions
Germany (DE)How to cite
APA:
Zweng, M., Fallavollita, P., Demirci, S., Kowarschik, M., Navab, N., & Mateus, D. (2015). Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising. In Ziv Yaniv, Cristian A. Linte, Pascal Fallavollita, Ziv Yaniv (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 114-123). Munich, DEU: Springer Verlag.
MLA:
Zweng, Markus, et al. "Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising." Proceedings of the 10th International Workshop on Augmented Environments for Computer-Assisted Interventions, AE-CAI 2015 and Held in Conjunction with, MICCAI 2015, Munich, DEU Ed. Ziv Yaniv, Cristian A. Linte, Pascal Fallavollita, Ziv Yaniv, Springer Verlag, 2015. 114-123.
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