Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.


Catheter Guidewire Tracking X-ray Fluoroscopy Deep learning Convolutional neural network Segmentation 


Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Biomedical Imaging Group Rotterdam, Department of Medical InformaticsErasmus MCRotterdamThe Netherlands
  2. 2.Philips HealthcareInterventional X-ray InnovationBestThe Netherlands
  3. 3.Department of Radiology and Nuclear MedicineErasmus MCRotterdamThe Netherlands
  4. 4.Imaging Physics, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands

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