{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:58:53Z","timestamp":1790287133945,"version":"4.1.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032382320","type":"print"},{"value":"9783032382337","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-38233-7_62","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:00:02Z","timestamp":1790283602000},"page":"659-669","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VigilSAM3: Reinforcement-Optimized Memory Gating for\u00a0Ultra-long Surgical Video Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3471-7041","authenticated-orcid":false,"given":"Xincheng","family":"Yao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4083-5144","authenticated-orcid":false,"given":"Yijun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9321-5603","authenticated-orcid":false,"given":"Zhuojin","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7199-9382","authenticated-orcid":false,"given":"Kangwei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0871-9581","authenticated-orcid":false,"given":"Haisu","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4721-7744","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3871-663X","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"62_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, F.A., Arsalan, M., Al-Ali, A., Al-Jalham, K., Balakrishnan, S.: Clip-rl: Surgical scene segmentation using contrastive language-vision pretraining & reinforcement learning. In: 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), pp. 863\u2013868. IEEE (2025)","DOI":"10.1109\/CBMS65348.2025.00175"},{"key":"62_CR2","unstructured":"Carion, N., et\u00a0al.: Sam 3: Segment anything with concepts. arXiv preprint arXiv:2511.16719 (2025)"},{"issue":"1","key":"62_CR3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41597-022-01719-2","volume":"10","author":"M Carstens","year":"2023","unstructured":"Carstens, M., et al.: The dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science. Sci. Data 10(1), 3 (2023)","journal-title":"Sci. Data"},{"key":"62_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, H.K., Oh, S.W., Price, B., Lee, J.Y., Schwing, A.: Putting the object back into video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3151\u20133161 (2024)","DOI":"10.1109\/CVPR52733.2024.00304"},{"key":"62_CR5","doi-asserted-by":"publisher","unstructured":"Cheng, H.K., Schwing, A.G.: Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, pp. 640\u2013658. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19815-1_37","DOI":"10.1007\/978-3-031-19815-1_37"},{"key":"62_CR6","first-page":"11781","volume":"34","author":"HK Cheng","year":"2021","unstructured":"Cheng, H.K., Tai, Y.W., Tang, C.K.: Rethinking space-time networks with improved memory coverage for efficient video object segmentation. Adv. Neural. Inf. Process. Syst. 34, 11781\u201311794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"62_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-030-59716-0_33","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"T Czempiel","year":"2020","unstructured":"Czempiel, T., et al.: TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 343\u2013352. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_33"},{"key":"62_CR8","doi-asserted-by":"crossref","unstructured":"Ding, S., et al.: Sam2long: Enhancing sam 2 for long video segmentation with a training-free memory tree. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13614\u201313624 (2025)","DOI":"10.1109\/ICCV51701.2025.01264"},{"key":"62_CR9","unstructured":"Hao, P., et al.: Surgery-r1: Advancing surgical-vqla with reasoning multimodal large language model via reinforcement learning. arXiv preprint arXiv:2506.19469 (2025)"},{"issue":"12","key":"62_CR10","doi-asserted-by":"publisher","first-page":"13145","DOI":"10.1109\/LRA.2025.3627088","volume":"10","author":"YJ Ho","year":"2025","unstructured":"Ho, Y.J., Chiu, Z.Y., Zhi, Y., Yip, M.C.: Surgirl: Toward life-long learning for surgical automation by incremental reinforcement learning. IEEE Robot. Autom. Lett. 10(12), 13145\u201313152 (2025)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"62_CR11","unstructured":"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)"},{"issue":"2","key":"62_CR12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"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. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"62_CR13","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"issue":"1","key":"62_CR14","doi-asserted-by":"publisher","first-page":"e29","DOI":"10.1097\/SLE.0000000000000244","volume":"26","author":"H Li","year":"2016","unstructured":"Li, H., Wei, Y.: Hepatic vein injuries during laparoscopic hepatectomy. Surg. Laparosc. Endosc. Percutaneous Tech. 26(1), e29\u2013e31 (2016)","journal-title":"Surg. Laparosc. Endosc. Percutaneous Tech."},{"key":"62_CR15","doi-asserted-by":"publisher","unstructured":"Lin, X., Xiang, Y., Yu, L., Yan, Z.: Beyond adapting sam: Towards end-to-end ultrasound image segmentation via auto prompting. In: Linguraru, M.G., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 24\u201334. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_3","DOI":"10.1007\/978-3-031-72111-3_3"},{"key":"62_CR16","unstructured":"Liu, A., et al.: Medsam3: Delving into segment anything with medical concepts. arXiv preprint arXiv:2511.19046 (2025)"},{"key":"62_CR17","doi-asserted-by":"publisher","unstructured":"Liu, H., et al.: Resurgsam2: Referring segment anything in surgical video via credible long-term tracking. In: Gee, J.C., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 435\u2013445, Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-032-05127-1_42","DOI":"10.1007\/978-3-032-05127-1_42"},{"key":"62_CR18","unstructured":"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)"},{"issue":"1","key":"62_CR19","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"62_CR20","unstructured":"Ma, J., et al.: Medsam2: Segment anything in 3d medical images and videos. arXiv preprint arXiv:2504.03600 (2025)"},{"issue":"9","key":"62_CR21","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1038\/s41551-017-0132-7","volume":"1","author":"L Maier-Hein","year":"2017","unstructured":"Maier-Hein, L., et al.: Surgical data science for next-generation interventions. Nat. Biomed. Eng. 1(9), 691\u2013696 (2017)","journal-title":"Nat. Biomed. Eng."},{"key":"62_CR22","unstructured":"Murali, A., et al.: The endoscapes dataset for surgical scene segmentation, object detection, and critical view of safety assessment: Official splits and benchmark. arXiv preprint arXiv:2312.12429 (2023)"},{"issue":"2","key":"62_CR23","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s00534-012-0556-0","volume":"20","author":"Y Otsuka","year":"2013","unstructured":"Otsuka, Y., et al.: Gas embolism in laparoscopic hepatectomy: what is the optimal pneumoperitoneal pressure for laparoscopic major hepatectomy? J. Hepatobiliary Pancreat. Sci. 20(2), 137\u2013140 (2013)","journal-title":"J. Hepatobiliary Pancreat. Sci."},{"key":"62_CR24","doi-asserted-by":"publisher","unstructured":"Qin, X., Dai, H., Hu, X., Fan, D.P., Shao, L., Van Gool, L.: Highly accurate dichotomous image segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, pp. 38\u201356. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_3","DOI":"10.1007\/978-3-031-19797-0_3"},{"key":"62_CR25","unstructured":"Ravi, N., et\u00a0al.: Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)"},{"key":"62_CR26","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"62_CR27","unstructured":"Shao, Z., et al.: Deepseekmath-v2: Towards self-verifiable mathematical reasoning. arXiv preprint arXiv:2511.22570 (2025)"},{"issue":"1","key":"62_CR28","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1111\/hpb.12077","volume":"16","author":"RI Troisi","year":"2014","unstructured":"Troisi, R.I., et al.: Risk factors and management of conversions to an open approach in laparoscopic liver resection: analysis of 265 consecutive cases. HPB 16(1), 75\u201382 (2014)","journal-title":"HPB"},{"key":"62_CR29","doi-asserted-by":"crossref","unstructured":"Videnovic, J., Lukezic, A., Kristan, M.: A distractor-aware memory for visual object tracking with sam2. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 24255\u201324264 (2025)","DOI":"10.1109\/CVPR52734.2025.02259"},{"key":"62_CR30","doi-asserted-by":"crossref","unstructured":"Wang, R., Sun, Y., Guo, J., Wu, H., Qin, J.: Gdkvm: Echocardiography video segmentation via spatiotemporal key-value memory with gated delta rule. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12191\u201312200 (2025)","DOI":"10.1109\/ICCV51701.2025.01133"},{"key":"62_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Y., Xing, Z., Yu, L., Fu, H., Huang, C., Zhu, L.: Vivim: a video vision mamba for ultrasound video segmentation. In: IEEE Transactions on Circuits and Systems for Video Technology (2025)","DOI":"10.1109\/TCSVT.2025.3563411"},{"key":"62_CR32","first-page":"2491","volume":"34","author":"Z Yang","year":"2021","unstructured":"Yang, Z., Wei, Y., Yang, Y.: Associating objects with transformers for video object segmentation. Adv. Neural. Inf. Process. Syst. 34, 2491\u20132502 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"62_CR33","doi-asserted-by":"publisher","unstructured":"Yao, X., et al.: Hrvvs: A high-resolution video vasculature segmentation network via hierarchical autoregressive residual priors. In: Gee, J.C., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 266\u2013276. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-032-05127-1_26","DOI":"10.1007\/978-3-032-05127-1_26"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38233-7_62","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:00:13Z","timestamp":1790283613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38233-7_62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032382320","9783032382337"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38233-7_62","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"assertion":[{"value":"25 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}