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Zweng, M.* ; Fallavollita, P.* ; Demirci, S.* ; Kowarschik, M.* ; Navab, N.* ; Mateus, D.

Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising.

Lect. Notes Comput. Sc. 9365, 114-123 (2015)
Postprint DOI
Open Access Green
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 10th International Workshop on Augmented Environments for Computer-Assisted Interventions, AE-CAI 2015
Konferzenzdatum 9th October 2015
Konferenzort Munich, Germany
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 9365, Heft: , Seiten: 114-123 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Computational Biology (ICB)