Abstract
Accurate localization of catheters or guidewires in fluoroscopy images is important to improve the stability of intervention procedures as well as the development of surgical navigation systems. Recently, deep learning methods have been proposed to improve performance, however these techniques require extensive pixel-wise annotations. Moreover, the human annotation effort is equally expensive. In this study, we mitigate this labeling effort using generative adversarial networks (cycleGAN) wherein we synthesize realistic catheters in flouroscopy from localized guidewires in camera images whose annotations are cheaper to acquire. Our approach is motivated by the fact that catheters are tubular structures with varying profiles, thus given a guidewire in a camera image, we can obtain the centerline that follows the profile of a catheter in an X-ray image and create plausible X-ray images composited with such a centerline. In order to generate an image similar to the actual X-ray image, we propose a loss term that includes perceptual loss alongside the standard cycle loss. Experimental results show that the proposed method has better performance than the conventional GAN and generates images with consistent quality. Further, we provide evidence to the development of methods that leverage such synthetic composite images in supervised settings.
Equally contributed by Mr. Ullah and Mr. Chikontwe.
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Notes
- 1.
Note that no annotations are present for the X-ray dataset. Figure 1(d) only highlights the guidewire position for clarity.
References
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Subramanian, V., Wang, H., Wu, J.T., Wong, K.C., Sharma, A., Syeda-Mahmood, T.: Automated detection and type classification of central venous catheters in chest X-rays. arXiv preprint arXiv:1907.01656 (2019)
Tmenova, O., Martin, R., Duong, L.: CycleGAN for style transfer in X-ray angiography. Int. J. Comput. Assist. Radiol. Surg. 1–10 (2019)
Uherčík, M., Kybic, J., Zhao, Y., Cachard, C., Liebgott, H.: Line filtering for surgical tool localization in 3D ultrasound images. Comput. Biol. Med. 43(12), 2036–2045 (2013)
Vandini, A., Glocker, B., Hamady, M., Yang, G.Z.: Robust guidewire tracking under large deformations combining segment-like features (SEGlets). Med. Image Anal. 38, 150–164 (2017)
Wagner, M.G., Laeseke, P., Speidel, M.A.: Deep learning based guidewire segmentation in X-ray images. In: Medical Imaging 2019: Physics of Medical Imaging. vol. 10948, p. 1094844. International Society for Optics and Photonics (2019)
Yi, X., Adams, S., Babyn, P., Elnajmi, A.: Automatic catheter and tube detection in pediatric X-ray images using a scale-recurrent network and synthetic data. J. Digit. Imaging 1–10 (2019)
Ying, X., Guo, H., Ma, K., Wu, J., Weng, Z., Zheng, Y.: X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10619–10628 (2019)
Zhang, T., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)
Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47–57 (2016)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgment
This work is supported by the Robot industry fusion core technology development project through the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry and Energy of Korea (MOTIE) (NO. 10052980) and the DGIST R & D Program of the Ministry of Science and ICT (19-RT-01).
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Ullah, I., Chikontwe, P., Park, S.H. (2019). Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks. In: Rekik, I., Adeli, E., Park, S. (eds) Predictive Intelligence in Medicine. PRIME 2019. Lecture Notes in Computer Science(), vol 11843. Springer, Cham. https://doi.org/10.1007/978-3-030-32281-6_13
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DOI: https://doi.org/10.1007/978-3-030-32281-6_13
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