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Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke, bleeding, and low illumination.
Ronneberger, O., Fischer, P., and Brox, T., “U-net: Convolutional networks for biomedical image segmentation,” in [ Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18
2015
Earlier work this paper cites.
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H., “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in [ Proceedings of the European conference on computer vision (ECCV)
2018
Earlier work this paper cites.
Hasan, S. K., Simon, R. A., and Linte, C. A., “Segmentation and removal of surgical instruments for background scene visualization from endoscopic/laparoscopic video,” in [ Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
2021
Earlier work this paper cites.
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., and Luo, P., “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in neural information processing systems
2021
Earlier work this paper cites.
Kitaguchi, D., Fujino, T., Takeshita, N., Hasegawa, H., Mori, K., and Ito, M., “Limited generalizability of single deep neural network for surgical instrument segmentation in different surgical environments,” Scientific reports
2022
Earlier work this paper cites.
Yang, Z., Simon, R., and Linte, C., “A weakly supervised learning approach for surgical instrument segmentation from laparoscopic video sequences,” in [ Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling
2022
Cited alongside, same era.
Lavanchy, J. L., Vardazaryan, A., Mascagni, P., Mutter, D., and Padoy, N., “Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos,” Scientific reports
2023
Cited alongside, same era.
Hayoz, M., Hahne, C., Gallardo, M., Candinas, D., Kurmann, T., Allan, M., and Sznitman, R., “Learning how to robustly estimate camera pose in endoscopic videos,” International journal of computer assisted radiology and surgery
2023
Cited alongside, same era.
Kirillov, A. et al., “Segment anything,” arXiv preprint arXiv:2304.02643
2023
Cited alongside, same era.
Rueckert, T., Rueckert, D., and Palm, C., “Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art,” Computers in Biology and Medicine
2024
Closest in time.
Ma, J., He, Y., Li, F., et al., “Segment anything in medical images,” Nature Communications
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
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Cheng, J., Ye, J., Deng, Z., et al., “Sam-med2d,” arXiv preprint arXiv:2308.16184
2023
Cited alongside, same era.