{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:05:18Z","timestamp":1790283918130,"version":"4.1.0"},"publisher-location":"Cham","reference-count":26,"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_9","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:58:16Z","timestamp":1790283496000},"page":"89-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CSAM-HQ: A Multi-stage Refinement Framework for\u00a0Surgical Instrument Segmentation Based on\u00a0SAM and\u00a0Probabilistic Graphical Models"],"prefix":"10.1007","author":[{"given":"Xueyi","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenzhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fucang","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changmiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianqiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huoling","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"issue":"1","key":"9_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10979-w","volume":"58","author":"FA Ahmed","year":"2024","unstructured":"Ahmed, F.A., et al.: Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review. Artif. Intell. Rev. 58(1), 1 (2024)","journal-title":"Artif. Intell. Rev."},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.media.2018.11.008","volume":"52","author":"H Al Hajj","year":"2019","unstructured":"Al Hajj, H., et al.: Cataracts: challenge on automatic tool annotation for cataract surgery. Med. Image Anal. 52, 24\u201341 (2019)","journal-title":"Med. Image Anal."},{"key":"9_CR3","unstructured":"Allan, M., et\u00a0al.: 2018 robotic scene segmentation challenge. arXiv preprint arXiv:2001.11190 (2020)"},{"key":"9_CR4","unstructured":"Allan, M., et\u00a0al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"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\u20136201 (2023)","DOI":"10.1109\/WACV56688.2023.00613"},{"issue":"1","key":"9_CR6","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/TMECH.2025.3584339","volume":"31","author":"Y Cao","year":"2026","unstructured":"Cao, Y., et al.: A review of critical deep learning-based image guidance technologies for surgical robots. IEEE\/ASME Trans. Mechatron. 31(1), 60\u201381 (2026)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"2509686","DOI":"10.1080\/24699322.2025.2509686","volume":"30","author":"A Comptour","year":"2025","unstructured":"Comptour, A., et al.: Retrospective case control study on the evaluation of the impact of augmented reality in gynecological laparoscopy on patients operated for myomectomy or adenomyomectomy. Comput. Assist. Surg. 30(1), 2509686 (2025)","journal-title":"Comput. Assist. Surg."},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Covaciu, F., Gherman, B., Rus, G., Vaida, C., Zima, I., Pisla, D.: Development of a virtual reality simulator for a robotic-assisted laparoscopic surgery. In: 2024 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), pp. 1\u20136 (2024)","DOI":"10.1109\/AQTR61889.2024.10554158"},{"key":"9_CR10","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"9_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/978-3-030-59716-0_57","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"C Gonz\u00e1lez","year":"2020","unstructured":"Gonz\u00e1lez, C., Bravo-S\u00e1nchez, L., Arbelaez, P.: ISINet: an instance-based approach for surgical instrument segmentation. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 595\u2013605. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_57"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Han, J., Eoh, K.J., An, M.C., Lee, S.H., Yang, S.: Enhancing endoscopic surgical imaging: AI-based smoke removal and image enhancement. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 7055\u20137057 (2024)","DOI":"10.1109\/BIBM62325.2024.10822601"},{"key":"9_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101994","volume":"70","author":"MK Hasan","year":"2021","unstructured":"Hasan, M.K., Calvet, L., Rabbani, N., Bartoli, A.: Detection, segmentation, and 3d pose estimation of surgical tools using convolutional neural networks and algebraic geometry. Med. Image Anal. 70, 101994 (2021)","journal-title":"Med. Image Anal."},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/TMRB.2022.3170215","volume":"4","author":"B Huang","year":"2022","unstructured":"Huang, B., et al.: Simultaneous depth estimation and surgical tool segmentation in laparoscopic images. IEEE Trans. Med. Rob. Bionics 4(2), 335\u2013338 (2022)","journal-title":"IEEE Trans. Med. Rob. Bionics"},{"key":"9_CR15","unstructured":"Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018)"},{"key":"9_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/978-3-030-32254-0_49","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Jin","year":"2019","unstructured":"Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 440\u2013448. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_49"},{"key":"9_CR17","doi-asserted-by":"publisher","first-page":"29914","DOI":"10.52202\/075280-1303","volume":"36","author":"L Ke","year":"2023","unstructured":"Ke, L., et al.: Segment anything in high quality. Adv. Neural Inf. Process. Syst. 36, 29914\u201329934 (2023)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"9_CR19","first-page":"109","volume":"24","author":"P Kr\u00e4henb\u00fchl","year":"2011","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. Adv. Neural Inf. Process. Syst. (NeurIPS) 24, 109\u2013117 (2011)","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"issue":"3","key":"9_CR20","doi-asserted-by":"publisher","first-page":"3350","DOI":"10.1109\/LRA.2026.3653373","volume":"11","author":"Y Ryu","year":"2026","unstructured":"Ryu, Y., Hong, J., Kee, H., Park, J., Park, S.: Deep learning-driven tumor boundary estimation using robotic palpation in minimally invasive surgery. IEEE Rob. Autom. Lett. 11(3), 3350\u20133357 (2026)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Song, R., Li, Y., Meng, M.Q.H., et\u00a0al.: Segmentation learning framework for multi-class surgical instrument. In: Efficient Medical Artificial Intelligence: First International Workshop, EMA4MICCAI 2025, Held in Conjunction with MICCAI 2025, p.\u00a0320 (2026)","DOI":"10.1007\/978-3-032-13961-0_32"},{"key":"9_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103387","volume":"99","author":"Q Wang","year":"2025","unstructured":"Wang, Q., Zhao, S., Xu, Z., Zhou, S.K.: Lacoste: exploiting stereo and temporal contexts for surgical instrument segmentation. Med. Image Anal. 99, 103387 (2025)","journal-title":"Med. Image Anal."},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.neunet.2023.11.055","volume":"170","author":"Y Yin","year":"2024","unstructured":"Yin, Y., Luo, S., Zhou, J., Kang, L., Chen, C.Y.C.: Ldcnet: lightweight dynamic convolution network for laparoscopic procedures image segmentation. Neural Netw. 170, 441\u2013452 (2024)","journal-title":"Neural Netw."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z.: Surgicalsam: efficient class promptable surgical instrument segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 6890\u20136898 (2024)","DOI":"10.1609\/aaai.v38i7.28514"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhang, Z., Tao, Y.: Surgical instrument segmentation network by sam and gradient feature. In: 2024 IEEE International conference on Medical Artificial Intelligence (MedAI), pp. 21\u201325 (2024)","DOI":"10.1109\/MedAI62885.2024.00010"},{"key":"9_CR26","unstructured":"Zhu, Z., et al.: Partsam: a scalable promptable part segmentation model trained on native 3d data. In: International Conference on Learning Representations (ICLR) (2026)"}],"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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:58:20Z","timestamp":1790283500000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38233-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032382320","9783032382337"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38233-7_9","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"}}]}}