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Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising.
Lect. Notes Comput. Sc. 9365, 114-123 (2015)
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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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
10th International Workshop on Augmented Environments for Computer-Assisted Interventions, AE-CAI 2015
Conference Date
9th October 2015
Conference Location
Munich, Germany
Quellenangaben
Volume: 9365,
Pages: 114-123
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute of Computational Biology (ICB)