{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:21:48Z","timestamp":1783099308518,"version":"3.54.6"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872014","type":"print"},{"value":"9783030872021","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87202-1_40","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T19:03:23Z","timestamp":1632337403000},"page":"415-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with\u00a0Transformer-Based Stereoscopic Depth Perception"],"prefix":"10.1007","author":[{"given":"Yonghao","family":"Long","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoshuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi Hang","family":"Yee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi Fai","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russell H.","family":"Taylor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Unberath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Dou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"40_CR1","unstructured":"Allan, M., et al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)"},{"issue":"6","key":"40_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/rcs.2149","volume":"16","author":"JM Ferguson","year":"2020","unstructured":"Ferguson, J.M., et al.: Comparing the accuracy of the da Vinci xi and da Vinci si for image guidance and automation. Int. J. Med. Robot. Comput. Assist. Surgery 16(6), 1\u201310 (2020)","journal-title":"Int. J. Med. Robot. Comput. Assist. Surgery"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Gao, W., Tedrake, R.: SurfelWarp: efficient non-volumetric single view dynamic reconstruction. arXiv preprint arXiv:1904.13073 (2019)","DOI":"10.15607\/RSS.2018.XIV.029"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 270\u2013279 (2017)","DOI":"10.1109\/CVPR.2017.699"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366\u20132369. IEEE (2010)","DOI":"10.1109\/ICPR.2010.579"},{"key":"40_CR6","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":"Yueming Jin","year":"2019","unstructured":"Jin, Yueming, Cheng, Keyun, Dou, Qi., Heng, Pheng-Ann.: 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":"40_CR7","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TRO.2020.3020739","volume":"37","author":"J Lamarca","year":"2020","unstructured":"Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37, 291\u2013303 (2020)","journal-title":"IEEE Trans. Robot."},{"issue":"5","key":"40_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1618452.1618521","volume":"28","author":"H Li","year":"2009","unstructured":"Li, H., Adams, B., Guibas, L.J., Pauly, M.: Robust single-view geometry and motion reconstruction. ACM Trans. Graph. (ToG) 28(5), 1\u201310 (2009)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"40_CR9","doi-asserted-by":"publisher","first-page":"3920","DOI":"10.1109\/TII.2020.3011067","volume":"17","author":"L Li","year":"2020","unstructured":"Li, L., Li, X., Yang, S., Ding, S., Jolfaei, A., Zheng, X.: Unsupervised learning-based continuous depth and motion estimation with monocular endoscopy for virtual reality minimally invasive surgery. IEEE Trans. Ind. Inform. 17, 3920\u20133928 (2020)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"40_CR10","doi-asserted-by":"publisher","first-page":"2294","DOI":"10.1109\/LRA.2020.2970659","volume":"5","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: SuPer: a surgical perception framework for endoscopic tissue manipulation with surgical robotics. IEEE Robot. Autom. Lett. 5(2), 2294\u20132301 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. arXiv preprint arXiv:2011.02910 (2020)","DOI":"10.1109\/ICCV48922.2021.00614"},{"key":"40_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-59716-0_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"X Liu","year":"2020","unstructured":"Liu, X., et al.: Reconstructing sinus anatomy from endoscopic video \u2013 towards a radiation-free approach for quantitative longitudinal assessment. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 3\u201313. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_1"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Lu, J., Jayakumari, A., Richter, F., Li, Y., Yip, M.C.: Super deep: a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction. arXiv preprint arXiv:2003.03472 (2020)","DOI":"10.1109\/ICRA48506.2021.9561249"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040\u20134048 (2016)","DOI":"10.1109\/CVPR.2016.438"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Fox, D., Seitz, S.M.: Dynamicfusion: reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343\u2013352 (2015)","DOI":"10.1109\/CVPR.2015.7298631"},{"key":"40_CR16","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)"},{"key":"40_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"Olaf Ronneberger","year":"2015","unstructured":"Ronneberger, Olaf, Fischer, Philipp, Brox, Thomas: U-Net: convolutional networks for biomedical image segmentation. In: Navab, Nassir, Hornegger, Joachim, Wells, William M.., Frangi, Alejandro F.. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"40_CR18","unstructured":"Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional (2010)"},{"key":"40_CR19","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"40_CR20","unstructured":"Song, J.: 3D non-rigid SLAM in minimally invasive surgery. Ph.D. thesis (2020)"},{"issue":"4","key":"40_CR21","doi-asserted-by":"publisher","first-page":"4068","DOI":"10.1109\/LRA.2018.2856519","volume":"3","author":"J Song","year":"2018","unstructured":"Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: real-time large-scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. Autom. Lett. 3(4), 4068\u20134075 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"4","key":"40_CR22","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/JDT.2008.926497","volume":"4","author":"D Stoyanov","year":"2008","unstructured":"Stoyanov, D., Mylonas, G.P., Lerotic, M., Chung, A.J., Yang, G.Z.: Intra-operative visualizations: perceptual fidelity and human factors. J. Display Technol. 4(4), 491\u2013501 (2008)","journal-title":"J. Display Technol."},{"key":"40_CR23","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1007\/978-3-319-32552-1_63","volume-title":"Springer Handbook of Robotics","author":"Russell H. Taylor","year":"2016","unstructured":"Taylor, Russell H.., Menciassi, Arianna, Fichtinger, Gabor, Fiorini, Paolo, Dario, Paolo: Medical robotics and computer-integrated surgery. In: Siciliano, Bruno, Khatib, Oussama (eds.) Springer Handbook of Robotics, pp. 1657\u20131684. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-32552-1_63"},{"key":"40_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"issue":"4","key":"40_CR25","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5515\u20135524 (2019)","DOI":"10.1109\/CVPR.2019.00566"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Ye, M., Johns, E., Handa, A., Zhang, L., Pratt, P., Yang, G.Z.: Self-supervised siamese learning on stereo image pairs for depth estimation in robotic surgery. arXiv preprint arXiv:1705.08260 (2017)","DOI":"10.31256\/HSMR2017.14"},{"issue":"11","key":"40_CR28","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/34.888718","volume":"22","author":"Z Zhang","year":"2000","unstructured":"Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330\u20131334 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87202-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T23:13:50Z","timestamp":1673306030000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87202-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872014","9783030872021"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87202-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"531","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}