Abstract
The recent success of convolutional neural networks in many computer vision tasks implies that their application could also be beneficial for vision tasks in cardiac electrophysiology procedures which are commonly carried out under guidance of C-arm fluoroscopy. Many efforts for catheter detection and reconstruction have been made, but especially robust detection of catheters in X-ray images in realtime is still not entirely solved. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate > 99 %. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of \(6.08\,\pm \,4.66\) mm.
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Baur, C., Albarqouni, S., Demirci, S., Navab, N., Fallavollita, P. (2016). CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_4
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