{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T12:24:01Z","timestamp":1774009441853,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["203145Z\/16\/Z"],"award-info":[{"award-number":["203145Z\/16\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["NS\/A000027\/1"],"award-info":[{"award-number":["NS\/A000027\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["WT101957"],"award-info":[{"award-number":["WT101957"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009614","name":"Petroleum Technology Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009614","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R004080\/1"],"award-info":[{"award-number":["EP\/R004080\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Fetoscopic laser photocoagulation is a minimally invasive procedure to treat twin-to-twin transfusion syndrome during pregnancy by stopping irregular blood flow in the placenta. Building an image mosaic of the placenta and its network of vessels could assist surgeons to navigate in the challenging fetoscopic environment during the procedure.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methodology<\/jats:title>\n                <jats:p>We propose a fetoscopic mosaicking approach by combining deep learning-based optical flow with robust estimation for filtering inconsistent motions that occurs due to floating particles and specularities. While the current state of the art for fetoscopic mosaicking relies on clearly visible vessels for registration, our approach overcomes this limitation by considering the motion of all consistent pixels within consecutive frames. We also overcome the challenges in applying off-the-shelf optical flow to fetoscopic mosaicking through the use of robust estimation and local refinement.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We compare our proposed method against the state-of-the-art vessel-based and optical flow-based image registration methods, and robust estimation alternatives. We also compare our proposed pipeline using different optical flow and robust estimation alternatives.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Through analysis of our results, we show that our method outperforms both the vessel-based state of the art and LK, noticeably when vessels are either poorly visible or too thin to be reliably identified. Our approach is thus able to build consistent placental vessel mosaics in challenging cases where currently available alternatives fail.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-022-02623-1","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T11:21:42Z","timestamp":1651576902000},"page":"1125-1134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Robust fetoscopic mosaicking from deep learned flow fields"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3716-3503","authenticated-orcid":false,"given":"Oluwatosin","family":"Alabi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1329-4565","authenticated-orcid":false,"given":"Sophia","family":"Bano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Vasconcelos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna L.","family":"David","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Deprest","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"2623_CR1","doi-asserted-by":"crossref","unstructured":"Aubry M, Maturana D, Efros AA, Russell BC, Sivic J (2014) Seeing 3D chairs: Exemplar part-based 2d-3d alignment using a large dataset of cad models. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2014.487"},{"key":"2623_CR2","unstructured":"Bano S, Casella A, Vasconcelos F, Moccia S, Attilakos G, Wimalasundera R, David A, Paladini D, Deprest J, Mattos L, Stoyanov D (2021) FetReg: Placental vessel segmentation and registration in fetoscopy challenge dataset"},{"key":"2623_CR3","doi-asserted-by":"crossref","unstructured":"Bano S, Vasconcelos F, Amo MT, Dwyer G, Gruijthuijsen C, Deprest J, Ourselin S, Vander\u00a0Poorten E, Vercauteren T, Stoyanov D (2019) Deep sequential mosaicking of fetoscopic videos. In: International conference on medical image computing and computer-assisted intervention, pp 311\u2013319. Springer","DOI":"10.1007\/978-3-030-32239-7_35"},{"key":"2623_CR4","doi-asserted-by":"crossref","unstructured":"Bano S, Vasconcelos F, Shepherd LM, Vander Poorten E, Vercauteren T, Ourselin S, David AL, Deprest J, Stoyanov D (2020) Deep placental vessel segmentation for fetoscopic mosaicking. In: Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L (eds) Medical image computing and computer assisted intervention \u2013 MICCAI 2020. Springer International Publishing, Cham, pp 763\u2013773","DOI":"10.1007\/978-3-030-59716-0_73"},{"key":"2623_CR5","doi-asserted-by":"publisher","unstructured":"Bano S, Vasconcelos F, Tella-Amo M, Dwyer G, Gruijthuijsen C, Vander Poorten E, Vercauteren T, Ourselin S, Deprest J, Stoyanov D (2020) Deep learning-based fetoscopic mosaicking for field-of-view expansion. Int J Comput Assist Radiol Surg. https:\/\/doi.org\/10.1007\/s11548-020-02242-8","DOI":"10.1007\/s11548-020-02242-8"},{"key":"2623_CR6","doi-asserted-by":"publisher","unstructured":"Baschat A, Chmait R, Deprest J, Gratac\u00f3s E, Hecher K, Kontopoulos E, Quintero R, Skupski D, Valsky D, Ville Y (2011) Twin-to-twin transfusion syndrome (ttts). J Perinat Med 39(2):107\u2013112. https:\/\/doi.org\/10.1515\/JPM.2010.147 (Copyright: Copyright 2011 Elsevier B.V., All rights reserved)","DOI":"10.1515\/JPM.2010.147"},{"key":"2623_CR7","doi-asserted-by":"publisher","unstructured":"Brown M, Hartley RI, Nister D (2007) Minimal solutions for panoramic stitching. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1\u20138. https:\/\/doi.org\/10.1109\/CVPR.2007.383082","DOI":"10.1109\/CVPR.2007.383082"},{"key":"2623_CR8","doi-asserted-by":"crossref","unstructured":"Butler DJ, Wulff J, Stanley GB, Black MJ (2012) A naturalistic open source movie for optical flow evaluation. In: European conference on computer vision, pp 611\u2013625. Springer","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"2623_CR9","doi-asserted-by":"crossref","unstructured":"Daga P, Chadebecq F, Shakir DI, Garc\u00eda-Peraza-Herrera LC, Tella M, Dwyer G, David A, Deprest J, Stoyanov D, Vercauteren T, Ourselin S (2016) Real-time mosaicing of fetoscopic videos using SIFT. In: Medical Imaging 2016: image-Guided Procedures, Robotic Interventions, and Modeling, vol. 9786, p 97861R. International Society for Optics and Photonics","DOI":"10.1117\/12.2217172"},{"key":"2623_CR10","unstructured":"DeTone D, Malisiewicz T, Rabinovich A (2016) Deep image homography estimation. CoRR abs\/1606.03798. arXiv:1606.03798"},{"key":"2623_CR11","doi-asserted-by":"crossref","unstructured":"Fischer P, Dosovitskiy A, Ilg E, H\u00e4usser P, Hazirbas C, Golkov V, van\u00a0der Smagt P, Cremers D, Brox T (2015) FlowNet: learning optical flow with convolutional networks. CoRR abs\/1504.06852. arXiv:1504.06852","DOI":"10.1109\/ICCV.2015.316"},{"issue":"6","key":"2623_CR12","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381\u2013395. https:\/\/doi.org\/10.1145\/358669.358692","journal-title":"Commun ACM"},{"key":"2623_CR13","doi-asserted-by":"publisher","unstructured":"Fortun D, Bouthemy P, Kervrann C (2015) Optical flow modeling and computation: a survey. Computer Vision and Image Understanding 134:1\u201321. https:\/\/doi.org\/10.1016\/j.cviu.2015.02.008. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1077314215000429. Image Understanding for Real-world Distributed Video Networks","DOI":"10.1016\/j.cviu.2015.02.008"},{"key":"2623_CR14","volume-title":"Multiple view geometry in computer vision","author":"R Hartley","year":"2003","unstructured":"Hartley R, Zisserman A (2003) Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, USA","edition":"2"},{"key":"2623_CR15","doi-asserted-by":"crossref","unstructured":"Ilg E, Mayer N, Saikia T, Keuper M, Dosovitskiy A, Brox T (2016) FlowNet 2.0: evolution of optical flow estimation with deep networks. CoRR abs\/1612.01925. arXiv:1612.01925","DOI":"10.1109\/CVPR.2017.179"},{"key":"2623_CR16","doi-asserted-by":"publisher","unstructured":"Kondermann D, Nair R, Honauer K, Krispin K, Andrulis J, Brock A, G\u00fcssefeld B, Rahimimoghaddam M, Hofmann S, Brenner C, J\u00e4hne B (2016) The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 19\u201328. https:\/\/doi.org\/10.1109\/CVPRW.2016.10","DOI":"10.1109\/CVPRW.2016.10"},{"issue":"4","key":"2623_CR17","doi-asserted-by":"publisher","first-page":"7831","DOI":"10.1109\/LRA.2021.3100938","volume":"6","author":"L Li","year":"2021","unstructured":"Li L, Bano S, Deprest J, David AL, Stoyanov D, Vasconcelos F (2021) Globally optimal fetoscopic mosaicking based on pose graph optimisation with affine constraints. IEEE Robot Autom Let 6(4):7831\u20137838. https:\/\/doi.org\/10.1109\/LRA.2021.3100938","journal-title":"IEEE Robot Autom Let"},{"key":"2623_CR18","doi-asserted-by":"crossref","unstructured":"Liu P, Lyu MR, King I, Xu J (2019) Selflow: self-supervised learning of optical flow. In: CVPR","DOI":"10.1109\/CVPR.2019.00470"},{"key":"2623_CR19","doi-asserted-by":"publisher","unstructured":"Lopriore E, Middeldorp JM, Oepkes D, Klumper FJ, Walther FJ, Vandenbussche FP (2007) Residual anastomoses after fetoscopic laser surgery in twin-to-twin transfusion syndrome: frequency, associated risks and outcome. Placenta 28(2,3), 204\u2013208. https:\/\/doi.org\/10.1016\/j.placenta.2006.03.005","DOI":"10.1016\/j.placenta.2006.03.005"},{"key":"2623_CR20","unstructured":"Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence IJCAI\u201981, vol 2. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 674\u2013679"},{"key":"2623_CR21","doi-asserted-by":"publisher","unstructured":"Mayer N, Ilg E, Hausser P, Fischer P, Cremers D, Dosovitskiy A, Brox T (2016) A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. pp 4040\u20134048. https:\/\/doi.org\/10.1109\/CVPR.2016.438","DOI":"10.1109\/CVPR.2016.438"},{"key":"2623_CR22","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"2623_CR23","volume-title":"Numerical optimization","author":"J Nocedal","year":"2006","unstructured":"Nocedal J, Wright SJ (2006) Numerical optimization, 2nd edn. Springer, New York, NY, USA","edition":"2"},{"key":"2623_CR24","doi-asserted-by":"crossref","unstructured":"Peter L, Tella-Amo M, Shakir DI, Attilakos G, Wimalasundera R, Deprest J, Ourselin S, Vercauteren T (2018) Retrieval and registration of long-range overlapping frames for scalable mosaicking of in vivo fetoscopy. CoRR abs\/1802.10554. arXiv:1802.10554","DOI":"10.1007\/s11548-018-1728-4"},{"key":"2623_CR25","doi-asserted-by":"publisher","unstructured":"Prince S (2012) Computer vision. Models, learning, and inference. Foreword by Andrew Fitzgibbon. https:\/\/doi.org\/10.1017\/CBO9780511996504","DOI":"10.1017\/CBO9780511996504"},{"key":"2623_CR26","unstructured":"Reeff M, Gerhard F, Cattin P, G\u00e1bor S (2006) Mosaicing of endoscopic placenta images. INFORMATIK 2006\u2013Informatik f\u00fcr Menschen, Band 1"},{"key":"2623_CR27","doi-asserted-by":"crossref","unstructured":"Shen X, Darmon F, Efros AA, Aubry M (2020) RANSAC-Flow: generic two-stage image alignment. In: ECCV","DOI":"10.1007\/978-3-030-58548-8_36"},{"key":"2623_CR28","doi-asserted-by":"crossref","unstructured":"Sun D, Yang X, Liu M, Kautz J (2017) PWC-Net: Cnns for optical flow using pyramid, warping, and cost volume. CoRR abs\/1709.02371. arXiv:1709.02371","DOI":"10.1109\/CVPR.2018.00931"},{"key":"2623_CR29","doi-asserted-by":"crossref","unstructured":"Teed Z, Deng J (2021) Raft: recurrent all-pairs field transforms for optical flow (extended abstract). In: Z.H. Zhou (Ed.) Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 4839\u20134843. International joint conferences on artificial intelligence organization. Sister Conferences Best Papers","DOI":"10.24963\/ijcai.2021\/662"},{"issue":"2","key":"2623_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.5.2.021217","volume":"5","author":"M Tella-Amo","year":"2018","unstructured":"Tella-Amo M, Peter L, Shakir DI, Deprest J, Stoyanov D, Iglesias JE, Vercauteren T, Ourselin S (2018) Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy. J Med Imag 5(2):1\u201316. https:\/\/doi.org\/10.1117\/1.JMI.5.2.021217","journal-title":"J Med Imag"},{"key":"2623_CR31","doi-asserted-by":"crossref","unstructured":"Triggs B, McLauchlan PF, Hartley RI, Fitzgibbon AW (2000) Bundle adjustment \u2013 a modern synthesis. In: Triggs B, Zisserman A, Szeliski R (eds) Vision algorithms: theory and practice. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 298\u2013372","DOI":"10.1007\/3-540-44480-7_21"},{"key":"2623_CR32","doi-asserted-by":"crossref","unstructured":"Weinzaepfel P, Revaud J, Harchaoui Z, Schmid C (2013) Deepflow: large displacement optical flow with deep matching. In: 2013 IEEE international conference on computer vision, pp 1385\u20131392","DOI":"10.1109\/ICCV.2013.175"}],"updated-by":[{"DOI":"10.1007\/s11548-023-03018-6","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000}}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02623-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-022-02623-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02623-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T15:15:56Z","timestamp":1696346156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-022-02623-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":32,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["2623"],"URL":"https:\/\/doi.org\/10.1007\/s11548-022-02623-1","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s11548-023-03018-6","asserted-by":"object"}]},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,3]]},"assertion":[{"value":"13 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11548-023-03018-6","URL":"https:\/\/doi.org\/10.1007\/s11548-023-03018-6","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Code for this paper can be found at .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"For this type of study, formal consent is not required.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article does not contain patient data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}