C. Baur, S. Albarqouni, S. Demirci, N. Navab, P. Fallavollita CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes 7th International Conference on Medical Imaging and Augmented Reality (MIAR), 24-26 August, 2016, Bern, Switzerland. (Best Paper Award) (bib)
	The recent success of convolutional neural networks in many computer vision tasks suggests 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 realtime and robust detection of catheters in X-ray images is still not entirely solved. We propose two novel methods for i) fully automatic electrophysiology catheter electrode detection in X-ray images and ii) depth estimation of such electrodes based on convolutional neural networks. For i), experiments on a total of 1650 X-ray images from 24 sequences 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 $6.08\pm4.66$mm.
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