CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9805)


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. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate > 99 %. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of \(6.08\,\pm \,4.66\) mm.


Convolutional neural network Catheter detection Depth prediction Electrophysiology Interventional imaging 


  1. 1.
    Albarqouni, S., Konrad, U., Wang, L., Navab, N., Demirci, S.: Single-view X-ray depth recovery: toward a novel concept for image-guided interventions. Int. J. Comput. Assist. Radiol. Surg. 11(6), 873–880 (2016)CrossRefGoogle Scholar
  2. 2.
    Baur, C., Milletari, F., Belagiannis, V., Navab, N., Fallavollita, P.: Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences. Int. J. Comput. Assist. Radiol. Surg. 11(7), 1319–1328 (2016)CrossRefGoogle Scholar
  3. 3.
    Belagiannis, V., Rupprecht, C., Carneiro, G., Navab, N.: Robust optimization for deep regression (2015). arXiv preprint arXiv:1505.06606
  4. 4.
    Brost, A., Liao, R., Strobel, N., Hornegger, J.: Respiratory motion compensation by model-based catheter tracking during EP procedures. Med. Image Anal. 14(5), 695–706 (2010)CrossRefGoogle Scholar
  5. 5.
    Demirci, S., Bigdelou, A., Wang, L., Wachinger, C., Baust, M., Tibrewal, R., Ghotbi, R., Eckstein, H.-H., Navab, N.: 3D stent recovery from one X-Ray projection. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 178–185. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in Neural Information Processing Systems, pp. 2366–2374 (2014)Google Scholar
  7. 7.
    Fallavollita, P.: Acquiring multiview C-arm images to assist cardiac ablation procedures. J. Image Video Process. 2010, 1–10, Article ID: 3 (2010). doi: 10.1155/2010/871409 Google Scholar
  8. 8.
    Fallavollita, P.: Is single-view fluoroscopy sufficient in guiding cardiac ablation procedures? J. Biomed. Imaging 2010, 1–13, Article ID: 631264 (2010). doi: 10.1155/2010/631264 Google Scholar
  9. 9.
    Franken, E., Rongen, P., van Almsick, M., ter Haar Romeny, B.M.: Detection of electrophysiology catheters in noisy fluoroscopy images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 25–32. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Hoffmann, M., Brost, A., Jakob, C., Bourier, F., Koch, M., Kurzidim, K., Hornegger, J., Strobel, N.: Semi-automatic catheter reconstruction from two views. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 584–591. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Hoffmann, M., Brost, A., Jakob, C., Koch, M., Bourier, F., Kurzidim, K., Hornegger, J., Strobel, N.: Reconstruction method for curvilinear structures from two views. In: SPIE Medical Imaging, p. 86712F. International Society for Optics and Photonics (2013)Google Scholar
  12. 12.
    Hoffmann, M., Brost, A., Koch, M., Bourier, F., Maier, A., Kurzidim, K., Strobel, N., Hornegger, J.: Electrophysiology catheter detection and reconstruction from two views in fluoroscopic images (2015)Google Scholar
  13. 13.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv preprint arXiv:1502.03167
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  15. 15.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  16. 16.
    Ma, Y.L., Gogin, N., Cathier, P., Housden, R.J., Gijsbers, G., Cooklin, M., O’Neill, M., Gill, J., Rinaldi, C.A., Razavi, R., et al.: Real-time X-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions. Med. Phys. 40(7), 071902 (2013)CrossRefGoogle Scholar
  17. 17.
    Ma, Y.: Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 391–399. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Milletari, F., Belagiannis, V., Navab, N., Fallavollita, P.: Fully automatic catheter localization in c-arm images using \(\ell \)1-sparse coding. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 570–577. Springer, Heidelberg (2014)Google Scholar
  19. 19.
    Milletari, F., Navab, N., Fallavollita, P.: Automatic detection of multiple and overlapping EP catheters in fluoroscopic sequences. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 371–379. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Schenderlein, M., Stierlin, S., Manzke, R., Rasche, V., Dietmayer, K.: Catheter tracking in asynchronous biplane fluoroscopy images by 3D B-snakes. In: SPIE Medical Imaging, p. 76251U. International Society for Optics and Photonics (2010)Google Scholar
  21. 21.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
  22. 22.
    Wen, W., Chen, T., Barbu, A., Wang, P., Strobel, N., Zhou, S.K., Comaniciu, D.: Learning-based hypothesis fusion for robust catheter tracking in 2D X-ray fluoroscopy. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1097–1104. IEEE (2011)Google Scholar
  23. 23.
    Wen, W., Chen, T., Strobel, N., Comaniciu, D.: Fast tracking of catheters in 2D fluoroscopic images using an integrated CPU-GPU framework. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1184–1187. IEEE (2012)Google Scholar
  24. 24.
    Yatziv, L., Chartouni, M., Datta, S., Sapiro, G.: Toward multiple catheters detection in fluoroscopic image guided interventions. IEEE Trans. Inf. Technol. Biomed. 16(4), 770–781 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer Aided Medical Procedures (CAMP)Technical University of MunichMunichGermany
  2. 2.Whiting School of EngineeringJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations