{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T13:42:59Z","timestamp":1752673379182,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100008050","name":"Fundaci\u00f3n Cellex","doi-asserted-by":"crossref","award":["LCF\/PR\/GN14\/10270005"],"award-info":[{"award-number":["LCF\/PR\/GN14\/10270005"]}],"id":[{"id":"10.13039\/100008050","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100008050","name":"Fundaci\u00f3n Cellex","doi-asserted-by":"publisher","award":["LCF\/PR\/GN18\/10310003"],"award-info":[{"award-number":["LCF\/PR\/GN18\/10310003"]}],"id":[{"id":"10.13039\/100008050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003030","name":"Ag\u00e8ncia de Gesti\u00f3 d\u2019Ajuts Universitaris i de Recerca","doi-asserted-by":"publisher","award":["2017 SGR n o 1531"],"award-info":[{"award-number":["2017 SGR n o 1531"]}],"id":[{"id":"10.13039\/501100003030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["MDM-2015-0502"],"award-info":[{"award-number":["MDM-2015-0502"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["ATTRACT project MIIFI"],"award-info":[{"award-number":["ATTRACT project MIIFI"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["BES-2017-081164"],"award-info":[{"award-number":["BES-2017-081164"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Departament de Salut","award":["SLT008\/18\/00156"],"award-info":[{"award-number":["SLT008\/18\/00156"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s11548-020-02256-2","type":"journal-article","created":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T19:02:33Z","timestamp":1600542153000},"page":"1869-1879","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5024-209X","authenticated-orcid":false,"given":"Enric","family":"Perera-Bel","sequence":"first","affiliation":[]},{"given":"Mario","family":"Ceresa","sequence":"additional","affiliation":[]},{"given":"Jordina","family":"Torrents-Barrena","sequence":"additional","affiliation":[]},{"given":"Narc\u00eds","family":"Masoller","sequence":"additional","affiliation":[]},{"given":"Brenda","family":"Valenzuela-Alcaraz","sequence":"additional","affiliation":[]},{"given":"Eduard","family":"Gratac\u00f3s","sequence":"additional","affiliation":[]},{"given":"Elisenda","family":"Eixarch","sequence":"additional","affiliation":[]},{"given":"Miguel A.","family":"Gonz\u00e1lez Ballester","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,19]]},"reference":[{"key":"2256_CR1","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/S0146-0005(05)80012-6","volume":"19","author":"K Benirschke","year":"1995","unstructured":"Benirschke K (1995) The biology of the twinning process: how placentation influences outcome. Semin Perinatol 19:342\u2013350","journal-title":"Semin Perinatol"},{"key":"2256_CR2","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/S0002-9378(00)70233-X","volume":"182","author":"ML Denbow","year":"2000","unstructured":"Denbow ML, Cox P, Taylor M, Hammal DM, Fisk NM (2000) Placental angioarchitecture in monochorionic twin pregnancies: relationship to fetal growth, fetofetal transfusion syndrome, and pregnancy outcome. Am J Obstet Gynecol 182:417\u2013426","journal-title":"Am J Obstet Gynecol"},{"key":"2256_CR3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1053\/ejpn.2001.0400","volume":"5","author":"F Haverkamp","year":"2001","unstructured":"Haverkamp F, Lex C, Hanisch C, Fahnenstich H, Zerres K (2001) Neurodevelopmental risks in twin-to-twin transfusion syndrome: preliminary findings. Eur J Paediatr Neourol 5:21\u201327","journal-title":"Eur J Paediatr Neourol"},{"key":"2256_CR4","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.ajog.2007.09.043","volume":"198","author":"AC Rossi","year":"2008","unstructured":"Rossi AC, D\u2019Addario V (2008) Laser therapy and serial amnioreduction as treatment for twin-twin transfusion syndrome: a metaanalysis and review of literature. Am J Obstet Gynecol 198:147\u2013152","journal-title":"Am J Obstet Gynecol"},{"key":"2256_CR5","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1056\/NEJMoa032597","volume":"351","author":"MV Senat","year":"2004","unstructured":"Senat MV, Jan Deprest, Boulvain M, Paupe A, Winer N, Ville Y (2004) Endoscopic laser surgery versus serial amnioreduction for severe twin-to-twin transfusion syndrome. N Engl J Med 351:136\u2013144","journal-title":"N Engl J Med"},{"key":"2256_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2018.10.003","volume":"51","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena J, Piella G, Masoller N, Gratac\u00f3s E, Eixarch E, Ceresa M, Gonz\u00e1lez Ballester MA (2019) Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal 51:61\u201388","journal-title":"Med Image Anal"},{"key":"2256_CR7","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.ultrasmedbio.2012.09.003","volume":"36","author":"SL Collins","year":"2013","unstructured":"Collins SL, Stevenson GN, Noble JA, Impey L (2013) Rapid calculation of standardized placental volume at 11 to 13 weeks and the prediction of small for gestational age babies. Ultrasound Med Biol 36:253\u2013260","journal-title":"Ultrasound Med Biol"},{"key":"2256_CR8","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1002\/uog.7609","volume":"36","author":"KB Cheong","year":"2010","unstructured":"Cheong KB, Leung KY, Li TKT, Chan HY, Lee YP, Tang MHY (2010) Comparison of inter- and intraobserver agreement and reliability between three different types of placental volume measurement technique (XI VOCAL\u2122, VOCAL\u2122 and multiplanar) and validity in the in-vitro setting. Ultrasound Obstet Gynecol 36:210\u2013217","journal-title":"Ultrasound Obstet Gynecol"},{"key":"2256_CR9","doi-asserted-by":"publisher","first-page":"3182","DOI":"10.1016\/j.ultrasmedbio.2015.07.021","volume":"41","author":"GN Stevenson","year":"2015","unstructured":"Stevenson GN, Collins SL, Ding J, Impey L, Noble JA (2015) 3-D ultrasound segmentation of the placenta using the random walker algorithm: reliability and agreement. Ultrasound Med Biol 41:3182\u20133193","journal-title":"Ultrasound Med Biol"},{"key":"2256_CR10","first-page":"279","volume":"2017","author":"P Looney","year":"2017","unstructured":"Looney P, Stevenson GN, Nicolaides KH, Plasencia W, Molloholli M, Natsis S (2017) Collins SL (2017) Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning. ISBI 2017:279\u2013282","journal-title":"ISBI"},{"key":"2256_CR11","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/TMI.2018.2858779","volume":"38","author":"X Yang","year":"2019","unstructured":"Yang X, Yu L, Li S, Wen H, Luo D, Bian C, Qin J, Ni D, Heng PA (2019) Towards automated semantic segmentation in prenatal volumetric ultrasound. Trans Med Imaging 38:180\u2013193","journal-title":"Trans Med Imaging"},{"key":"2256_CR12","doi-asserted-by":"crossref","unstructured":"Oguz BU, Wang J, Yushkevich N, Pouch A, Gee J, Yushkevich PA, Schwartz N, Oguz I (2018) Combining deep learning and multi-atlas label fusion for automated placenta segmentation from 3DUS. In: International workshop on preterm, perinatal and paediatric image analysis. Springer, Cham, pp 138\u2013148","DOI":"10.1007\/978-3-030-00807-9_14"},{"key":"2256_CR13","doi-asserted-by":"crossref","unstructured":"Alansary A, Kamnitsas K, Davidson A, Khlebnikov R, Rajchl M, Malamateniou C, Rutherford M, Hajnal JV, Glocker B, Rueckert D, Kainz B (2016) Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: International conference on medical image computing and computer-assisted intervention, pp 589\u2013597","DOI":"10.1007\/978-3-319-46723-8_68"},{"key":"2256_CR14","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.media.2016.04.009","volume":"34","author":"G Wang","year":"2016","unstructured":"Wang G, Zuluaga MA, Pratt R, Aertsen M, Doel T, Klusmann M, David AL, Deprest J, Vercauteren T, Ourselin S (2016) Slic-Seg: a minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views. Med Image Anal 34:137\u2013147","journal-title":"Med Image Anal"},{"key":"2256_CR15","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","volume":"41","author":"G Wang","year":"2018","unstructured":"Wang G, Zuluaga MA, Li W, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) DeepIGeoS: a deep interactive geodesic framework for med. image segmentation. Trans Pattern Anal Mach Intell 41:1559\u20131572","journal-title":"Trans Pattern Anal Mach Intell"},{"key":"2256_CR16","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.media.2019.03.008","volume":"54","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena J, Piella G, Masoller N, Gratac\u00f3s E, Eixarch E, Ceresa M, Gonz\u00e1lez Ballester MA (2019) Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med Image Anal 54:263\u2013279","journal-title":"Med Image Anal"},{"key":"2256_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.cmpb.2018.02.001","volume":"158","author":"S Moccia","year":"2018","unstructured":"Moccia S, De Momi E, El Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms\u2014review of methods, datasets and evaluation metrics. Comput Methods Programs Biomed 158:71\u201391","journal-title":"Comput Methods Programs Biomed"},{"key":"2256_CR18","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/TMI.2002.801166","volume":"21","author":"D Selle","year":"2002","unstructured":"Selle D, Preim B, Schenk A, Peitgen HO (2002) Analysis of vasculature for liver surgical planning. Trans. on Med. Imaging 21:1344\u20131357","journal-title":"Trans. on Med. Imaging"},{"key":"2256_CR19","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S1361-8415(01)00040-8","volume":"5","author":"LM Lorigo","year":"2001","unstructured":"Lorigo LM, Faugeras OD, Grimson WEL, Keriven R, Kikinis R, Nabavi A, Westin CF (2001) CURVES: curve evolution for vessel segmentation. Med Image Anal 5:195\u2013200","journal-title":"Med Image Anal"},{"key":"2256_CR20","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1152\/ajpheart.00510.2010","volume":"300","author":"MY Rennie","year":"2011","unstructured":"Rennie MY, Detmar J, Whiteley KJ, Yang J, Jurisicova A, Adamson SL, Sled JG (2011) Vessel tortuousity and reduced vascularization in the fetoplacental arterial tree after maternal exposure to polycyclic aromatic hydrocarbons. Am J Physiol Heart Circ Physiol 300:675\u2013684","journal-title":"Am J Physiol Heart Circ Physiol"},{"key":"2256_CR21","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.1113\/JP274845","volume":"596","author":"LS Cahill","year":"2018","unstructured":"Cahill LS, Rennie MY, Hoggarth J, Yu LX, Rahman A, Kingdom JC, Seed M, Macgowan CK, Sled JG (2018) Feto- and utero-placental vascular adaptations to chronic maternal hypoxia in the mouse. J Physiol 596:3285\u20133297","journal-title":"J Physiol"},{"issue":"8","key":"2256_CR22","doi-asserted-by":"publisher","first-page":"2440","DOI":"10.1109\/TIP.2015.2417683","volume":"24","author":"Y Cheng","year":"2015","unstructured":"Cheng Y, Hu X, Wang J, Wang Y, Tamura S (2015) Accurate vessel segmentation with constrained B-snake. Trans Image Process 24(8):2440\u20132455","journal-title":"Trans Image Process"},{"key":"2256_CR23","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TBME.2009.2032161","volume":"57","author":"S Esneault","year":"2010","unstructured":"Esneault S, Lafon C, Dillenseger JL (2010) Liver vessels segmentation using a hybrid geometrical moments\/graph cuts method. Trans BioMed Eng 57:276\u2013283","journal-title":"Trans BioMed Eng"},{"key":"2256_CR24","doi-asserted-by":"crossref","unstructured":"Meijs M, Manniesing R (2018) Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network. Med. Imaging 2018: Computer-Aided Diagn., SPIE","DOI":"10.1117\/12.2292974"},{"key":"2256_CR25","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.ejmp.2016.04.003","volume":"32","author":"YZ Zeng","year":"2016","unstructured":"Zeng YZ, Zhao YQ, Liao M, Zou BJ, Wang XF, Wang W (2016) Liver vessel segmentation based on extreme learning machine. Phys Medica 32:709\u2013716","journal-title":"Phys Medica"},{"key":"2256_CR26","doi-asserted-by":"crossref","unstructured":"Anghel C, Archer K, Chang JM, Cochran A, Radulescu A, Salafia CM, Turner R, Djima KY, Zhong L (2018) Placental vessel extraction with Shearlets, Laplacian Eigenmaps, and a conditional generative adversarial network. In: Understanding complex biological systems with mathematics. Springer, Cham, pp 171\u2013196","DOI":"10.1007\/978-3-319-98083-6_8"},{"key":"2256_CR27","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.media.2018.03.010","volume":"46","author":"K L\u00f3pez-Linares","year":"2018","unstructured":"L\u00f3pez-Linares K, Aranjuelo N, Kabongo L, Maclair G, Lete N, Ceresa M, Garc\u00eda-Familiar A, Mac\u00eda I, Gonz\u00e1lez Ballester MA (2018) Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. Med Image Anal 46:202\u2013214","journal-title":"Med Image Anal"},{"key":"2256_CR28","first-page":"903435","volume":"9034","author":"P Guo","year":"2014","unstructured":"Guo P, Wang Q, Wang X, Hao Z, Xu K, Ren H, Kim JB, Hwang Y (2014) Robust vessel detection and segmentation in ultrasound images by a data-driven approach. SPIE Med Imaging 9034:903435","journal-title":"SPIE Med Imaging"},{"key":"2256_CR29","first-page":"122","volume":"2011","author":"C Schneider","year":"2011","unstructured":"Schneider C, Guerrero J, Nguan C, Rohling R, Salcudean S (2011) Intra-operative \u201cPick-Up\u201d ultrasound for robot assisted surgery with vessel extraction and registration: a feasibility study. IPCAI 2011:122\u2013132","journal-title":"IPCAI"},{"key":"2256_CR30","doi-asserted-by":"publisher","first-page":"104993","DOI":"10.1016\/j.cmpb.2019.104993","volume":"179","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena J, L\u00f3pez-Velazco R, Piella G, Masoller N, Valenzuela-Alcaraz B, Gratac\u00f3s E, Eixarch E, Ceresa M, Gonz\u00e1lez Ballester MA (2019) TTTS-GPS: patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery. Comput Methods Programs Biomed 179:104993","journal-title":"Comput Methods Programs Biomed"},{"key":"2256_CR31","doi-asserted-by":"crossref","unstructured":"Torrents-Barrena J, Piella G, Masoller N, Gratacos E, Eixarch E, Ceresa M, Gonzalez Ballester MA (2019) Automatic segmentation of the placenta and its peripheral vasculature in volumetric ultrasound for TTTS fetal surgery. In: 2019 IEEE 16th international symposium on biomedical imaging. (ISBI 2019), pp 772\u2013775","DOI":"10.1109\/ISBI.2019.8759296"},{"key":"2256_CR32","first-page":"1768","volume":"28","author":"L Grady","year":"2006","unstructured":"Grady L (2006) Random Walks for Image Segmentation. Trans. on Pattern Anal. and Mach. Intell. 28:1768\u20131783","journal-title":"Intell."},{"key":"2256_CR33","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/TIP.2014.2302892","volume":"23","author":"S Jianbing","year":"2014","unstructured":"Jianbing S, Yunfan D, Wenguan W, Xuelong L (2014) Lazy random walks for superpixel segmentation. Trans Med Image Process 23:1451\u20131462","journal-title":"Trans Med Image Process"},{"key":"2256_CR34","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.imavis.2016.07.005","volume":"54","author":"ER Pujadas","year":"2016","unstructured":"Pujadas ER, Kjer HM, Piella G, Gonz\u00e1lez Ballester MA (2016) Iterated random walks with shape prior. Image Vis Comput 54:12\u201321","journal-title":"Image Vis Comput"},{"key":"2256_CR35","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3389\/fninf.2014.00013","volume":"8","author":"M McCormick","year":"2014","unstructured":"McCormick M, Liu X, Jomier J, Marion C, Ibanez L (2014) ITK: enabling reproducible research and open science. Front Neuroinform 8:13","journal-title":"Front Neuroinform"},{"key":"2256_CR36","unstructured":"Guennebaud G, Jacob B, Bossart R, Gomez Ferrero D, Nuentsa D et al (2010) Eigen 3. http:\/\/eigen.tuxfamily.org"},{"issue":"2","key":"2256_CR37","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1137\/0913035","volume":"13","author":"HA van der Vorst","year":"1992","unstructured":"van der Vorst HA (1992) Bi-CGSTAB: A fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems. SIAM J Sci Stat Comput 13(2):631\u2013644","journal-title":"SIAM J Sci Stat Comput"},{"key":"2256_CR38","doi-asserted-by":"crossref","unstructured":"Goc\u0142awski J, Weffgli\u0144ski T, Fabija\u0144ska A (2015) Accelerating the 3D random walker image segmentation algorithm by image graph reduction and GPU computing. In: Image processing communications challenges 6. Springer, Cham, pp 45\u201352","DOI":"10.1007\/978-3-319-10662-5_6"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02256-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02256-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02256-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T23:48:55Z","timestamp":1632008935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02256-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,19]]},"references-count":38,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["2256"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02256-2","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"type":"print","value":"1861-6410"},{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2020,9,19]]},"assertion":[{"value":"20 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards (PIC-39-16, HCB\/2016\/0138).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}