Abstract
Catheter ablation is a prevalent procedure for treating atrial fibrillation, primarily utilizing catheters equipped with electrodes to gather electrophysiological signals. However, the localization of catheters in fluoroscopy images presents a challenge for clinicians due to the complexity of the intervention processes. In this paper, we propose SIX-Net, a novel algorithm intending to localize landmarks of electrodes in fluoroscopy images precisely, by mixing up spatial-context information from three aspects: First, we propose a new network architecture specially designed for global-local spatial feature aggregation; Then, we mix up spatial correlations between segmentation and landmark detection, by sequential connections between the two tasks with the help of the Segment Anything Model; Finally, a weighted loss function is carefully designed considering the relative spatial-arrangement information among electrodes in the same image. Experiment results on the test set and two clinical-challenging subsets reveal that our method outperforms several state-of-the-art landmark detection methods (\({\sim } 50\% \) improvement for RF and \( {\sim } 25\%\) improvement for CS).
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References
Ambrosini, P., Ruijters, D., Niessen, W.J., Moelker, A., van Walsum, T.: Fully automatic and real-time catheter segmentation in X-ray fluoroscopy (2017)
Bodart, L.E., et al.: Technical and clinical study of X-ray-based surface echo probe tracking using an attached fiducial apparatus. Med. Phys. 48(5), 2528–2542 (2020)
Chang, P.L., et al.: Robust catheter and guidewire tracking using b-spline tube model and pixel-wise posteriors. IEEE Robot. Autom. Lett. 1(1), 303–308 (2016)
Chen, R., et al.: Semi-supervised anatomical landmark detection via shape-regulated self-training. Neurocomputing 471, 335–345 (2022)
Demoustier, M., Zhang, Y., Narasimha Murthy, V., Ghesu, F.C., Comaniciu, D.: Contrack: contextual transformer for device tracking in X-ray. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14228, pp. 679–688. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43996-4_65
Huang, L., Liu, Y., Chen, L., Chen, E.Z., Chen, X., Sun, S.: Robust landmark-based stent tracking in X-ray fluoroscopy. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13682, pp. 201–216. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20047-2_12
Jafari, M.H., et al.: U-land: uncertainty-driven video landmark detection. IEEE Trans. Med. Imaging 41(4), 793–804 (2022). https://doi.org/10.1109/TMI.2021.3123547
Kirillov, A., et al.: Segment anything (2023)
Lian, C., et al.: Multi-task dynamic transformer network for concurrent bone segmentation and large-scale landmark localization with dental CBCT. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 807–816. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_78
Ma, Y., et al.: Real-time X-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions. Med. Phys. (2013). https://doi.org/10.1118/1.4808114
Mark, D.B., et al.: Effect of catheter ablation vs medical therapy on quality of life among patients with atrial fibrillation: the CABANA randomized clinical trial. JAMA 321(13), 1275–1285 (2019). https://doi.org/10.1001/jama.2019.0692
McCouat, J., Voiculescu, I.: Contour-hugging heatmaps for landmark detection. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20565–20573 (2022). https://doi.org/10.1109/CVPR52688.2022.01994
Nguyen, A., et al.: End-to-end real-time catheter segmentation with optical flow-guided warping during endovascular intervention. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9967–9973 (2020). https://doi.org/10.1109/ICRA40945.2020.9197307
Parameswaran, R., Al-Kaisey, A.M., Kalman, J.M.: Catheter ablation for atrial fibrillation: current indications and evolving technologies. Nat. Rev. Cardiol. 18(3), 210–225 (2021)
Payer, C., Štern, D., Bischof, H., Urschler, M.: Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Med. Image Anal. 54, 207–219 (2019)
Quan, Q., Yao, Q., Li, J., Zhou, S.K.: Which images to label for few-shot medical landmark detection? (2021)
Ramadani, A., Bui, M., Wendler, T., Schunkert, H., Ewert, P., Navab, N.: A survey of catheter tracking concepts and methodologies. Med. Image Anal. 82, 102584 (2022). https://doi.org/10.1016/j.media.2022.102584. https://www.sciencedirect.com/science/article/pii/S1361841522002225
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. Int. J. Comput. Vision 77, 157–173 (2008)
Staerk, L., Sherer, J.A., Ko, D., Benjamin, E.J., Helm, R.H.: Atrial fibrillation: epidemiology, pathophysiology, and clinical outcomes. Circ. Res. 120(9), 1501–1517 (2017)
Torabinia, M., et al.: Deep learning-driven catheter tracking from bi-plane X-ray fluoroscopy of 3D printed heart phantoms. Mini-Invasive Surg. (2021). https://api.semanticscholar.org/CorpusID:237815143
Ullah, I., Chikontwe, P., Park, S.H.: Real-time tracking of guidewire robot tips using deep convolutional neural networks on successive localized frames. IEEE Access 7, 159743–159753 (2019). https://doi.org/10.1109/ACCESS.2019.2950263
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.: Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1451–1460. IEEE (2018)
Wang, X., Xu, Z., Yao, Q., Sun, Y., Zhou, S.K.: OFELIA: optical flow-based electrode localization. In: Submitted to Medical Imaging with Deep Learning (2024, under review). https://openreview.net/forum?id=8245ExLB4I
Yang, H., Shan, C., Kolen, A.F., N. de With, P.H.: Automated catheter localization in volumetric ultrasound using 3D patch-wise U-net with focal loss. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1346–1350 (2019). https://doi.org/10.1109/ICIP.2019.8803045
Yao, Q., He, Z., Han, H., Zhou, S.K.: Miss the point: targeted adversarial attack on multiple landmark detection (2020)
Yao, Q., Quan, Q., Xiao, L., Zhou, S.K.: One-shot medical landmark detection (2021)
Zhu, H., Yao, Q., Xiao, L., Zhou, S.K.: You only learn once: universal anatomical landmark detection. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 85–95. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_9
Zhu, H., Yao, Q., Xiao, L., Zhou, S.K.: Learning to localize cross-anatomy landmarks in X-ray images with a universal model. BME Front. 2022 (2022)
Acknowledgments
This study was funded by Natural Science Foundation of China under Grant 62271465 and Open Fund Project of Guangdong Academy of Medical Sciences, China (No. YKYKF202206).
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Wang, X., Xu, Z., Zhu, H., Yao, Q., Sun, Y., Zhou, S.K. (2024). SIX-Net: Spatial-Context Information miX-up for Electrode Landmark Detection. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15001. Springer, Cham. https://doi.org/10.1007/978-3-031-72378-0_32
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