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Accurate tracking of tissues and instruments in videos is crucial for Robotic-Assisted Minimally Invasive Surgery (RAMIS), as it enables the robot to comprehend the surgical scene with precise locations and interactions of tissues and tools.
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M. Yip and N. Das, “Robot autonomy for surgery,” in The Encyclopedia of MEDICAL ROBOTICS: Volume 1 Minimally Invasive Surgical Robotics . World Scientific, 2019, pp. 281–313
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2024
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A. Schmidt, O. Mohareri, S. DiMaio, M. C. Yip, and S. E. Salcudean, “Tracking and mapping in medical computer vision: A review,” Medical Image Analysis , p. 103131, 2024
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M. Neoral, J. Šerỳch, and J. Matas, “Mft: Long-term tracking of every pixel,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 6837–6847
2024
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J. Cartucho, A. Weld, S. Tukra, H. Xu, H. Matsuzaki, T. Ishikawa, M. Kwon, Y. E. Jang, K.-J. Kim, G. Lee, et al. , “Surgt challenge: Benchmark of soft-tissue trackers for robotic surgery,” Medical image analysis , vol. 91, p. 102985, 2024
2024
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A. Schmidt, O. Mohareri, S. DiMaio, and S. E. Salcudean, “Surgical tattoos in infrared: A dataset for quantifying tissue tracking and mapping,” IEEE Transactions on Medical Imaging , 2024
2024
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2024
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W. Yue, J. Zhang, K. Hu, Y. Xia, J. Luo, and Z. Wang, “Surgicalsam: Efficient class promptable surgical instrument segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 7, 2024, pp. 6890–6898
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