MaDetectDepthInstruments

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

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


ProjectForm
Title: A framework for Detection and Depth Prediction for Medical Instruments in Interventional 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 the guidance of C-arm fluoroscopy. In previous work, we managed to train a CNN that able to 1. detect catheters 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.
Student:  
Director: Prof. Nassir Navab
Supervisor: Shadi Albarqouni
Type: Master Thesis
Area: Industrial Tracking, Machine Learning, Medical Imaging
Status: finished
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.6 - 02 Jan 2019 - 11:32 - TobiasLasser