MaCathNets

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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.

Results so far

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)

Students.ProjectForm
Title: Depth Estimation for Catheters from Single-View Interventional X-ray Imaging
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 detect catheters tips and estimate the depth.
Student: Christoph Baur
Director: Prof. Nassir Navab
Supervisor: Shadi Albarqouni, Stefanie Demirci, Pascal Fallavollita
Type: Project
Area: Machine Learning, Medical Imaging
Status: finished
Start:  
Finish:  
Thesis (optional):  
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