Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
- 8.2k Downloads
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.
KeywordsCatheter Guidewire Tracking X-ray Fluoroscopy Deep learning Convolutional neural network Segmentation
Conflict of Interest
The authors declare that they have no conflict of interest.
- 3.Baur, C., Albarqouni, S., Demirci, S., Navab, N., Fallavollita, P.: CathNets: detection and single-view depth prediction of catheter electrodes. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, S.-L. (eds.) MIAR 2016. LNCS, vol. 9805, pp. 38–49. Springer, Cham (2016). doi: 10.1007/978-3-319-43775-0_4CrossRefGoogle Scholar
- 5.Chen, B.J., Wu, Z., Sun, S., Zhang, D., Chen, T.: Guidewire tracking using a novel sequential segment optimization method in interventional X-ray videos. In: Proceedings of IEEE International Symposium Biomedical Imaging, pp. 103–106 (2016)Google Scholar
- 6.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Computer Society Conference on computer vision and pattern recognition, pp. 770–778 (2016)Google Scholar
- 8.Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of ICML, vol. 37, pp. 448–456 (2015)Google Scholar
- 9.Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on CVPR, pp. 3431–3440 (2015)Google Scholar
- 10.Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of International Conference on 3D Vision, pp. 565–571. IEEE (2016)Google Scholar
- 14.Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)
- 16.Wagner, M.G., Strother, C.M., Mistretta, C.A.: Guidewire path tracking and segmentation in 2D fluoroscopic time series using device paths from previous frames. In: Proceedings of SPIE, p. 97842B (2016)Google Scholar
- 17.Wang, P., Chen, T., Zhu, Y., Zhang, W., Zhou, S., Comaniciu, D.: Robust guidewire tracking in fluoroscopy. In: Proceedings of IEEE Computer Society CVPR, pp. 691–698 (2009)Google Scholar