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

Abstract : In neuro-interventional surgeries, physicians rely on fluoro-scopic 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 6mm.
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https://hal.inria.fr/hal-01695947
Contributor : Diana Mateus <>
Submitted on : Monday, January 29, 2018 - 10:09:23 PM
Last modification on : Monday, January 29, 2018 - 10:09:23 PM

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  • HAL Id : hal-01695947, version 1

Citation

Markus Zweng, Pascal Fallavolita, Stefanie Demirci, Markus Kowarschik, Nassir Navab, et al.. Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising. AE-CAI Workshop at the International Conference on Medical Image Computing and Computer Aided Interventions, Oct 2015, Munich, Germany. ⟨hal-01695947⟩

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