Built on

Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N.: Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE transactions on medical imaging 36
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV (2017)
2017
Earlier work this paper cites.
Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Original
2017
Earlier work this paper cites.
Al Hajj, H., Lamard, M., Conze, P.-H., Roychowdhury, S., Hu, X., Maršalkaitė, G., Zisimopoulos, O., Dedmari, M.A., Zhao, F., Prellberg, J., et al
2019
Earlier work this paper cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV, pp. 213–229 (2020). Springer
2020
Earlier work this paper cites.
Hong, W.-Y., Kao, C.-L., Kuo, Y.-H., Wang, J.-R., Chang, W.-L., Shih, C.-S.: Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80. arXiv preprint arXiv:2012.12453 (2020)
Original
2020
Earlier work this paper cites.
Wang, J., Jin, Y., Wang, L., Cai, S., Heng, P.-A., Qin, J.: Efficient global-local memory for real-time instrument segmentation of robotic surgical video. In: MICCAI, pp. 341–351 (2021). Springer
2021
Earlier work this paper cites.
Zhao, Z., Jin, Y., Chen, J., Lu, B., Ng, C.-F., Liu, Y.-H., Dou, Q., Heng, P.-A.: Anchor-guided online meta adaptation for fast one-shot instrument segmentation from robotic surgical videos. MedIA 74
2021
Earlier work this paper cites.
Grammatikopoulou, M., Flouty, E., Kadkhodamohammadi, A., Quellec, G., Chow, A., Nehme, J., Luengo, I., Stoyanov, D.: Cadis: Cataract dataset for surgical rgb-image segmentation. MedIA 71
2021
Earlier work this paper cites.
Rivoir, D., Pfeiffer, M., Docea, R., Kolbinger, F., Riediger, C., Weitz, J., Speidel, S.: Long-term temporally consistent unpaired video translation from simulated surgical 3d data. In: ICCV, pp. 3343–3353 (2021)
2021
Earlier work this paper cites.

Similar

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18
2021
Cited alongside, same era.
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR, pp. 1290–1299 (2022)
2022
Cited alongside, same era.
Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: an empirical study on generalization, robustness and adaptation. In: MICCAI, pp. 234–244 (2023). Springer
2023
Cited alongside, same era.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al
2023
Cited alongside, same era.
Ghamsarian, N., El-Shabrawi, Y., Nasirihaghighi, S., Putzgruber-Adamitsch, D., Zinkernagel, M., Wolf, S., Schoeffmann, K., Sznitman, R.: Cataract-1k: Cataract surgery dataset for scene segmentation, phase recognition, and irregularity detection. arXiv preprint arXiv:2312.06295 (2023)

Then

Chen, T., Lu, A., Zhu, L., Ding, C., Yu, C., Ji, D., Li, Z., Sun, L., Mao, P., Zang, Y.: Sam2-adapter: Evaluating & adapting segment anything 2 in downstream tasks: Camouflage, shadow, medical image segmentation, and more. arXiv preprint arXiv:2408.04579 (2024)
Original
2024
Later among the works it cites.
Sheng, Y., Bano, S., Clarkson, M.J., Islam, M.: Surgical-desam: decoupling sam for instrument segmentation in robotic surgery. IJCARS, 1–5 (2024)
2024
Later among the works it cites.
Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. In: AAAI, vol. 38, pp. 6890–6898 (2024)
2024
Later among the works it cites.
Wu, Z., Schmidt, A., Kazanzides, P., Salcudean, S.E.: Real-time surgical instrument segmentation in video using point tracking and segment anything. arXiv preprint arXiv:2403.08003 (2024)
Original
2024
Later among the works it cites.

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2025

SASVi -- Segment Any Surgical Video

Sivakumar, Ssharvien Kumar, Frisch, Yannik, Ranem, Amin et al.

Understand

Purpose: Foundation models, trained on multitudes of public datasets, often require additional fine-tuning or re-prompting mechanisms to be applied to visually distinct target domains such as surgical videos.

Further, without domain knowledge, they cannot model the specific semantics of the target domain.
Hence, when applied to surgical video segmentation, they fail to generalise to sections where previously tracked objects leave the scene or new objects enter.
Methods: We propose SASVi, a novel re-prompting mechanism based on a frame-wise Mask R-CNN Overseer model, which is trained on a minimal amount of scarcely available annotations for the target domain.

Built on

Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N.: Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE transactions on medical imaging 36
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV (2017)
2017
Earlier work this paper cites.
Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Original
2017
Earlier work this paper cites.
Al Hajj, H., Lamard, M., Conze, P.-H., Roychowdhury, S., Hu, X., Maršalkaitė, G., Zisimopoulos, O., Dedmari, M.A., Zhao, F., Prellberg, J., et al
2019
Earlier work this paper cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV, pp. 213–229 (2020). Springer
2020
Earlier work this paper cites.
Hong, W.-Y., Kao, C.-L., Kuo, Y.-H., Wang, J.-R., Chang, W.-L., Shih, C.-S.: Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80. arXiv preprint arXiv:2012.12453 (2020)
Original
2020
Earlier work this paper cites.
Wang, J., Jin, Y., Wang, L., Cai, S., Heng, P.-A., Qin, J.: Efficient global-local memory for real-time instrument segmentation of robotic surgical video. In: MICCAI, pp. 341–351 (2021). Springer
2021
Earlier work this paper cites.
Zhao, Z., Jin, Y., Chen, J., Lu, B., Ng, C.-F., Liu, Y.-H., Dou, Q., Heng, P.-A.: Anchor-guided online meta adaptation for fast one-shot instrument segmentation from robotic surgical videos. MedIA 74
2021
Earlier work this paper cites.
Grammatikopoulou, M., Flouty, E., Kadkhodamohammadi, A., Quellec, G., Chow, A., Nehme, J., Luengo, I., Stoyanov, D.: Cadis: Cataract dataset for surgical rgb-image segmentation. MedIA 71
2021
Earlier work this paper cites.
Rivoir, D., Pfeiffer, M., Docea, R., Kolbinger, F., Riediger, C., Weitz, J., Speidel, S.: Long-term temporally consistent unpaired video translation from simulated surgical 3d data. In: ICCV, pp. 3343–3353 (2021)
2021
Earlier work this paper cites.

Similar

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18
2021
Cited alongside, same era.
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR, pp. 1290–1299 (2022)
2022
Cited alongside, same era.
Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: an empirical study on generalization, robustness and adaptation. In: MICCAI, pp. 234–244 (2023). Springer
2023
Cited alongside, same era.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al
2023
Cited alongside, same era.
Ghamsarian, N., El-Shabrawi, Y., Nasirihaghighi, S., Putzgruber-Adamitsch, D., Zinkernagel, M., Wolf, S., Schoeffmann, K., Sznitman, R.: Cataract-1k: Cataract surgery dataset for scene segmentation, phase recognition, and irregularity detection. arXiv preprint arXiv:2312.06295 (2023)

Then

Chen, T., Lu, A., Zhu, L., Ding, C., Yu, C., Ji, D., Li, Z., Sun, L., Mao, P., Zang, Y.: Sam2-adapter: Evaluating & adapting segment anything 2 in downstream tasks: Camouflage, shadow, medical image segmentation, and more. arXiv preprint arXiv:2408.04579 (2024)
Original
2024
Later among the works it cites.
Sheng, Y., Bano, S., Clarkson, M.J., Islam, M.: Surgical-desam: decoupling sam for instrument segmentation in robotic surgery. IJCARS, 1–5 (2024)
2024
Later among the works it cites.
Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. In: AAAI, vol. 38, pp. 6890–6898 (2024)
2024
Later among the works it cites.
Wu, Z., Schmidt, A., Kazanzides, P., Salcudean, S.E.: Real-time surgical instrument segmentation in video using point tracking and segment anything. arXiv preprint arXiv:2403.08003 (2024)
Original
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2023

Cited alongside, same era.

Frisch, Y., Fuchs, M., Mukhopadhyay, A.: Temporally consistent sequence-to-sequence translation of cataract surgeries. IJCARS 18

2023

Cited alongside, same era.

Sanner, A.P., Grauhan, N.F., Brockmann, M.A., Othman, A.E., Mukhopadhyay, A.: Detection of intracranial hemorrhage for trauma patients. arXiv preprint arXiv:2408.10768 (2024)

Original

2024

Cited alongside, same era.

Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications 15

2024

Cited alongside, same era.

Yu, J., Wang, A., Dong, W., Xu, M., Islam, M., Wang, J., Bai, L., Ren, H.: Sam 2 in robotic surgery: An empirical evaluation for robustness and generalization in surgical video segmentation. arXiv preprint arXiv:2408.04593 (2024)

Original

2024

Cited alongside, same era.

Liu, H., Zhang, E., Wu, J., Hong, M., Jin, Y.: Surgical sam 2: Real-time segment anything in surgical video by efficient frame pruning. arXiv preprint arXiv:2408.07931 (2024)

Original

2024