TY - GEN
T1 - Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising
AU - Zweng, Markus
AU - Fallavollita, Pascal
AU - Demirci, Stefanie
AU - Kowarschik, Markus
AU - Navab, Nassir
AU - Mateus, Diana
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84951292036&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24601-7_12
DO - 10.1007/978-3-319-24601-7_12
M3 - Conference contribution
AN - SCOPUS:84951292036
SN - 9783319246000
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 114
EP - 123
BT - Augmented Environments for Computer-Assisted Interventions - 10th International Workshop, AE-CAI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Yaniv, Ziv
A2 - Linte, Cristian A.
A2 - Fallavollita, Pascal
A2 - Yaniv, Ziv
PB - Springer Verlag
T2 - 10th International Workshop on Augmented Environments for Computer-Assisted Interventions, AE-CAI 2015 and Held in Conjunction with, MICCAI 2015
Y2 - 9 October 2015 through 9 October 2015
ER -