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Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery (2404.14040v1)

Published 22 Apr 2024 in cs.CV

Abstract: Purpose: The recent Segment Anything Model (SAM) has demonstrated impressive performance with point, text or bounding box prompts, in various applications. However, in safety-critical surgical tasks, prompting is not possible due to (i) the lack of per-frame prompts for supervised learning, (ii) it is unrealistic to prompt frame-by-frame in a real-time tracking application, and (iii) it is expensive to annotate prompts for offline applications. Methods: We develop Surgical-DeSAM to generate automatic bounding box prompts for decoupling SAM to obtain instrument segmentation in real-time robotic surgery. We utilise a commonly used detection architecture, DETR, and fine-tuned it to obtain bounding box prompt for the instruments. We then empolyed decoupling SAM (DeSAM) by replacing the image encoder with DETR encoder and fine-tune prompt encoder and mask decoder to obtain instance segmentation for the surgical instruments. To improve detection performance, we adopted the Swin-transformer to better feature representation. Results: The proposed method has been validated on two publicly available datasets from the MICCAI surgical instruments segmentation challenge EndoVis 2017 and 2018. The performance of our method is also compared with SOTA instrument segmentation methods and demonstrated significant improvements with dice metrics of 89.62 and 90.70 for the EndoVis 2017 and 2018. Conclusion: Our extensive experiments and validations demonstrate that Surgical-DeSAM enables real-time instrument segmentation without any additional prompting and outperforms other SOTA segmentation methods.

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References (14)
  1. Ma, J., Wang, B.: Segment anything in medical images. arXiv preprint arXiv:2304.12306 (2023) Carion et al. [2020] Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213–229 (2020). Springer He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021) Rezatofighi et al. [2019] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213–229 (2020). Springer He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021) Rezatofighi et al. [2019] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Liu et al. [2021] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021) Rezatofighi et al. [2019] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021) Rezatofighi et al. [2019] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
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Springer, Berlin, Heidelberg (2023) Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021) Rezatofighi et al. [2019] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. 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Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. 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[2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. 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Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. 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Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  5. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) González et al. [2020] González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. 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Springer, Berlin, Heidelberg (2023) González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. 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Springer, Berlin, Heidelberg (2023) Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. 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IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. 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[2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  6. González, C., Bravo-Sánchez, L., Arbelaez, P.: Isinet: an instance-based approach for surgical instrument segmentation. In: Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–605 (2020). Springer Iglovikov and Shvets [2018] Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. 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  7. Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018) Jin et al. [2019] Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. 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[2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  8. Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, pp. 440–448 (2019). Springer Zhao et al. [2020] Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  9. Zhao, Z., Jin, Y., Gao, X., Dou, Q., Heng, P.-A.: Learning motion flows for semi-supervised instrument segmentation from robotic surgical video. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 679–689 (2020). Springer Meinhardt et al. [2022] Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. 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In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  10. Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8844–8854 (2022) Zhao et al. [2022] Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  11. Zhao, Z., Jin, Y., Heng, P.-A.: Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11186–11193 (2022). IEEE Baby et al. [2023] Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  12. Baby, B., et al.: From forks to forceps: A new framework for instance segmentation of surgical instruments. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6191–6201 (2023) Yue et al. [2023] Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  13. Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: Efficient class promptable surgical instrument segmentation. arXiv preprint arXiv:2308.08746 (2023) Wang et al. [2023] Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023) Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
  14. Wang, A., Islam, M., Xu, M., Zhang, Y., Ren, H.: Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, pp. 234–244. Springer, Berlin, Heidelberg (2023)
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