Depth Estimation for Catheters from Single-View Interventional X-ray Imaging

Guided Research Project
Thesis by: Christoph Baur
Advisor: Prof. Nassir Navab
Supervision: Shadi Albarqouni, Stefanie Demirci, Pascal Fallavollita

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. In this project, we aim to build a CNN that able to 1) detect catheter's tips in real-time and 2)estimate the depth of detected electrodes.

Literature

2016
	S. Albarqouni, U. Konrad, L. Wang, N. Navab, S. Demirci Single-View X-Ray Depth Recovery: Towards a Novel Concept for Image-Guided Interventions International Journal of Computer Assisted Radiology and Surgery (IJCARS), 2016, June 2016, Volume 11, Issue 6, pp 873-880. (bib)

F. Milletari, V. Belagiannis, N. Navab, P. Fallavollita Fully automatic catheter localization in C-arm images using l1- Sparse Coding Proceedings of the 17th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Boston, September 2014 (bib)

C. Baur, F. Milletari, V. Belagiannis, N. Navab, P. Fallavollita Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences The 6th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) (bib)

Resultant Paper

2016
	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)