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We introduce a shape-sensitive loss function for catheter and guidewire segmentation and utilize it in a vision transformer network to establish a new state-of-the-art result on a large-scale X-ray images dataset.
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Y. Thakur, J. S. Bax, D. W. Holdsworth, and M. Drangova.: Design and performance evaluation of a remote catheter navigation system. In: IEEE Transactions on Biomedical Engineering (2009)
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H. Rafii-Tari, C. J. Payne, G.-Z. Yang.: Current and emerging robot-assisted endovascular catheterization technologies: A review. In: Annals of Biomedical Engineering (2014)
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Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Cardoso. : Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pages 240–248. Springer (2017)
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Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, and Ali Gholipour. : Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: International Workshop on Machine Learning in Medical Imaging, pages 379–387. Springer (2017)
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N. Simaan, R. M. Yasin, and L. Wang.: Medical technologies and challenges of robot-assisted minimally invasive intervention and diagnostics. In: Annual Review of Control, Robotics, and Autonomous System (2018)
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Ken CL Wong, Mehdi Moradi, Hui Tang, and Tanveer Syeda-Mahmood. : 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 612–619. Springer (2018)
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Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang.: UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In: arXiv:cs.CV (2018)
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M. E. M. K. Abdelaziz, D. Kundrat, M. Pupillo, G. Dagnino, T. MY, W. C. Kwok, V. Groenhuis, F. J. Siepel, C. Riga, S. Stramigioli, et al.: Toward a versatile robotic platform for fluoroscopy and MRI-guided endovascular interventions: A pre-clinical study. In: IROS (2019)
2019
Gherardini M, Mazomenos E, Menciassi A, Stoyanov D.: Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets. Compute Methods Programs Biomed, 192:105420, August 2020. doi: 10.1016/j.cmpb.2020.105420. Epub 2020 Feb 29. PMID: 32171151; PMCID: PMC7903142
2020
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H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y.-W. Chen, and J. Wu.: UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. In: arXiv:eess.IV (2020)
2020
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2021
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Cited alongside, same era.
Y. Zhao, S. Guo, Y. Wang, J. Cui, Y. Ma, Y. Zeng, X. Liu, Y. Jiang, Y. Li, L. Shi, et al.: A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot. In: Medical & Biological Engineering & Computing (2019)
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M. Benavente Molinero, G. Dagnino, J. Liu, W. Chi, M. Abdelaziz, T. Kwok, C. Riga, and G. Yang.: Haptic Guidance for Robot-Assisted Endovascular Procedures: Implementation and Evaluation on Surgical Simulator. In: IROS (2019)
2019
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Shuai Guo, Songyuan Tang, Jianjun Zhu, Jingfan Fan, Danni Ai, Hong Song, Ping Liang, Jian Yang.: Improved U-Net for Guidewire Tip Segmentation in X-ray Fluoroscopy Images. In: ICAIP ’19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing (2019)
2019
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Nabila Abraham and Naimul Mefraz Khan. : A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pages 683–687. IEEE (2019)
2019
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Davood Karimi and Septimiu E. Salcudean. : Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks. In: IEEE Transactions on Medical Imaging, 39(2):499–513 (2019)
2019
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Saeid Asgari Taghanaki, Yefeng Zheng, S Kevin Zhou, Bogdan Georgescu, Puneet Sharma, Daguang Xu, Dorin Comaniciu, and Ghassan Hamarneh. : Combo loss: Handling input and output imbalance in multi-organ segmentation. In: Computerized Medical Imaging and Graphics, 75:24–33 (2019)
2019
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W. Chi, G. Dagnino, T. Kwok, A. Nguyen, D. Kundrat, E. M. K. Abdelaziz, C. Riga, C. Bicknell, and G.-Z. Yang.: Collaborative robot-assisted endovascular catheterization with generative adversarial imitation learning In: ICRA, (2020)
2020
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S. Jadon.: A survey of loss functions for semantic segmentation. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Oct. (2020)
2020
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2021
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Q. Huang, Y. Zhou, and L. Tao.: Dual-Term Loss Function For Shape-Aware Medical Image Segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1798-1802 (2021), doi: 10.1109/ISBI48211.2021.9433775
2021
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W. Wang, Q. Li, C. Xiao, D. Zhang, L. Miao, and L. Wang.: An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation. In: Sensors, vol. 21, no. 8, article no. 2615, (2021) DOI: 10.3390/s21082615
2021
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J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou.: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. In: CoRR (2021)
2021
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2022
Later among the works it cites.
Huang, Baoru, Jian-Qing Zheng, Anh Nguyen, Chi Xu, Ioannis Gkouzionis, Kunal Vyas, David Tuch, Stamatia Giannarou, and Daniel S. Elson. Self-supervised depth estimation in laparoscopic image using 3D geometric consistency. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 13-22. Cham: Springer Nature Switzerland, 2022
2022
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Tran, Minh Q., Tuong Do, Huy Tran, Erman Tjiputra, Quang D. Tran, and Anh Nguyen. Light-weight deformable registration using adversarial learning with distilling knowledge. IEEE transactions on medical imaging 41, no. 6 (2022): 1443-1453
2022
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E. K. Aghdam, R. Azad, M. Zarvani, and D. Merhof.: Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation. In: arXiv:eess.IV (2022)
2022
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Huang, Baoru, Yicheng Hu, Anh Nguyen, Stamatia Giannarou, and Daniel S. Elson. Detecting the Sensing Area of a Laparoscopic Probe in Minimally Invasive Cancer Surgery. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 260-270. Cham: Springer Nature Switzerland, 2023
2023
Closest in time.
Ghosh, Rahul, et al. Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning. In: Journal of NeuroInterventional Surgery (2023).
2023
Closest in time.