{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T14:28:53Z","timestamp":1773671333677,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164484","type":"print"},{"value":"9783031164491","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16449-1_2","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T08:04:54Z","timestamp":1663315494000},"page":"13-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Self-supervised Depth Estimation in\u00a0Laparoscopic Image Using 3D Geometric Consistency"],"prefix":"10.1007","author":[{"given":"Baoru","family":"Huang","sequence":"first","affiliation":[]},{"given":"Jian-Qing","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Chi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ioannis","family":"Gkouzionis","sequence":"additional","affiliation":[]},{"given":"Kunal","family":"Vyas","sequence":"additional","affiliation":[]},{"given":"David","family":"Tuch","sequence":"additional","affiliation":[]},{"given":"Stamatia","family":"Giannarou","sequence":"additional","affiliation":[]},{"given":"Daniel S.","family":"Elson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"2_CR1","unstructured":"Allan, M., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv:2101.01133 (2021)"},{"issue":"11","key":"2_CR2","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robot. Res. 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"key":"2_CR3","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":"2_CR4","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3828\u20133838 (2019)","DOI":"10.1109\/ICCV.2019.00393"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Ambrus, R., Pillai, S., Raventos, A., Gaidon, A.: 3d packing for self-supervised monocular depth estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2485\u20132494 (2020)","DOI":"10.1109\/CVPR42600.2020.00256"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"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\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"2_CR7","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. Robot. Bion. 4(2), 335\u2013338 (2022)","journal-title":"IEEE Trans. Med. Robot. Bion."},{"issue":"8","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1007\/s11548-020-02205-z","volume":"15","author":"B Huang","year":"2020","unstructured":"Huang, B., et al.: Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe. Int. J. Comput. Assist. Radiol. Surg. 15(8), 1389\u20131397 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02205-z","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Huang, B., Zheng, J.Q., Giannarou, S., Elson, D.S.: H-net: unsupervised attention-based stereo depth estimation leveraging epipolar geometry. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460\u20134467 (2022)","DOI":"10.1109\/CVPRW56347.2022.00492"},{"key":"2_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/978-3-030-87202-1_22","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"B Huang","year":"2021","unstructured":"Huang, B., et al.: Self-supervised generative adversarial network for depth estimation in\u00a0laparoscopic images. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 227\u2013237. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_22"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Johnston, A., Carneiro, G.: Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4756\u20134765 (2020)","DOI":"10.1109\/CVPR42600.2020.00481"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Jung, H., Park, E., Yoo, S.: Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12642\u201312652 (2021)","DOI":"10.1109\/ICCV48922.2021.01241"},{"key":"2_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/978-3-030-58565-5_35","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Klingner","year":"2020","unstructured":"Klingner, M., Term\u00f6hlen, J.-A., Mikolajczyk, J., Fingscheidt, T.: Self-supervised monocular depth estimation: solving the dynamic object problem by semantic guidance. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 582\u2013600. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_35"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., Deng, J.: Raft-stereo: multilevel recurrent field transforms for stereo matching. In: 2021 International Conference on 3D Vision (3DV), pp. 218\u2013227. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00032"},{"issue":"5","key":"2_CR15","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1109\/TMI.2019.2950936","volume":"39","author":"X Liu","year":"2019","unstructured":"Liu, X., et al.: Dense depth estimation in monocular endoscopy with self-supervised learning methods. IEEE Trans. Med. Imaging 39(5), 1438\u20131447 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5695\u20135703 (2016)","DOI":"10.1109\/CVPR.2016.614"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Mahjourian, R., Wicke, M., Angelova, A.: Unsupervised learning of depth and ego-motion from monocular video using 3D geometric constraints. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 5667\u20135675 (2018)","DOI":"10.1109\/CVPR.2018.00594"},{"key":"2_CR18","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Icml (2010)"},{"key":"2_CR19","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"key":"2_CR20","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":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.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":"2_CR21","unstructured":"Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings Third International conference on 3-D Digital Imaging and Modeling, pp. 145\u2013152. IEEE (2001)"},{"key":"2_CR22","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/TMI.2022.3141013","volume":"41","author":"MQ Tran","year":"2022","unstructured":"Tran, M.Q., Do, T., Tran, H., Tjiputra, E., Tran, Q.D., Nguyen, A.: Light-weight deformable registration using adversarial learning with distilling knowledge. IEEE Trans. Med. Imaging 41, 1443\u20131453 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Xu, Y., Aliaga, D.G.: Robust pixel classification for 3d modeling with structured light. In: Proceedings of Graphics Interface 2007, pp. 233\u2013240 (2007)","DOI":"10.1145\/1268517.1268556"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16449-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:51:57Z","timestamp":1709830317000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16449-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164484","9783031164491"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16449-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}