A framework for Detection and Depth Prediction for Medical Instruments in Interventional Imaging

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 the guidance of C-arm fluoroscopy. In previous work, we managed to train a CNN that able to 1) detect catheter's tips in real-time and 2)estimate the depth of detected electrodes, however, few things need to be improved: 1) high-resolution detection map, 2) graphical model and make it work on noisy images.

Tasks

• create ground truth for new dataset (low dose X-ray images)
• train a CNN model for high resolution detection map
• ...

Literature

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)

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