{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:46:00Z","timestamp":1789029960331,"version":"build-2803163510"},"reference-count":57,"publisher":"IOP Publishing","issue":"9","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"crossref","award":["955956"],"award-info":[{"award-number":["955956"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2023,5,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    fast and accurate contouring of daily 3D images is a prerequisite for online adaptive radiotherapy. Current automatic techniques rely either on contour propagation with registration or deep learning (DL) based segmentation with convolutional neural networks (CNNs). Registration lacks general knowledge about the appearance of organs and traditional methods are slow. CNNs lack patient-specific details and do not leverage the known contours on the planning computed tomography (CT). This works aims to incorporate patient-specific information into CNNs to improve their segmentation accuracy.\n                    <jats:italic>Approach.<\/jats:italic>\n                    patient-specific information is incorporated into CNNs by retraining them solely on the planning CT. The resulting patient-specific CNNs are compared to general CNNs and rigid and deformable registration for contouring of organs-at-risk and target volumes in the thorax and head-and-neck regions.\n                    <jats:italic>Results.<\/jats:italic>\n                    patient-specific fine-tuning of CNNs significantly improves contour accuracy compared to standard CNNs. The method further outperforms rigid registration and a commercial DL segmentation software and yields similar contour quality as deformable registration (DIR). It is additionally 7\u201310 times faster than DIR.\n                    <jats:italic>Significance.<\/jats:italic>\n                    patient-specific CNNs are a fast and accurate contouring technique, enhancing the benefits of adaptive radiotherapy.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/accaca","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T18:42:06Z","timestamp":1680720126000},"page":"095010","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Patient-specific neural networks for contour propagation in online adaptive radiotherapy"],"prefix":"10.1088","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2874-3634","authenticated-orcid":true,"given":"A","family":"Smolders","sequence":"first","affiliation":[{"name":"Paul Scherrer Institute"},{"name":"Department of Physics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Lomax","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institute"},{"name":"Department of Physics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"DC","family":"Weber","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institute"},{"name":"University Hospital Zurich"},{"name":"University of Bern"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F","family":"Albertini","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"key":"pmbaccacabib1","doi-asserted-by":"publisher","first-page":"4399","DOI":"10.1088\/0031-9155\/56\/14\/011","type":"journal-article","article-title":"Is it necessary to plan with safety margins for actively scanned proton therapy?","volume":"56","author":"Albertini","year":"2011","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib2","doi-asserted-by":"publisher","first-page":"20190594","DOI":"10.1259\/bjr.20190594","type":"journal-article","article-title":"Online daily adaptive proton therapy","volume":"93","author":"Albertini","year":"2020","journal-title":"Br. J. Radiol."},{"key":"pmbaccacabib3","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abf8f5","type":"journal-article","article-title":"An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer","volume":"66","author":"Amstutz","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib4","doi-asserted-by":"publisher","first-page":"R363","DOI":"10.1088\/0031-9155\/51\/13\/R21","type":"journal-article","article-title":"IMRT: a review and preview","volume":"51","author":"Bortfeld","year":"2006","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib5","doi-asserted-by":"publisher","first-page":"e43\u2013e76","DOI":"10.1002\/mp.12256","type":"journal-article","article-title":"Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM Radiation Therapy Committee Task Group No. 132: Report","volume":"44","author":"Brock","year":"2017","journal-title":"Med. Phys."},{"key":"pmbaccacabib6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/1748-717X-7-32","type":"journal-article","article-title":"3D Variation in delineation of head and neck organs at risk","volume":"7","author":"Brouwer","year":"2012","journal-title":"Radiat. Oncol."},{"key":"pmbaccacabib7","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.565","type":"other","article-title":"One-shot video object segmentation","author":"Caelles","year":"2017"},{"key":"pmbaccacabib8","doi-asserted-by":"publisher","first-page":"23002","DOI":"10.1038\/s41598-021-02330-y","type":"journal-article","article-title":"A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer","volume":"11","author":"Chen","year":"2021","journal-title":"Sci. Rep."},{"key":"pmbaccacabib9","doi-asserted-by":"publisher","first-page":"488\u2013 496","DOI":"10.1002\/mp.15352","type":"journal-article","article-title":"Intentional deep overfit learning (IDOL): a novel deep learning strategy for adaptive radiation therapy","volume":"49","author":"Chun","year":"2022","journal-title":"Medical Physics"},{"key":"pmbaccacabib10","doi-asserted-by":"publisher","first-page":"61 \u2013 70","DOI":"10.1016\/j.radonc.2022.10.029","type":"journal-article","article-title":"Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system","volume":"177","author":"Costea","year":"2022","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib11","doi-asserted-by":"publisher","first-page":"9057","DOI":"10.3390\/ijerph19159057","type":"journal-article","article-title":"Clinical validation of a deep-learning segmentation software in head and neck: an early analysis in a developing radiation oncology center","volume":"19","author":"D\u2019Aviero","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"pmbaccacabib12","doi-asserted-by":"publisher","first-page":"4557","DOI":"10.1088\/0031-9155\/56\/14\/021","type":"journal-article","article-title":"Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study","volume":"56","author":"Deeley","year":"2011","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib13","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.1002\/mp.13620","type":"journal-article","article-title":"Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer","volume":"46","author":"Elmahdy","year":"2019","journal-title":"Med. Phys."},{"key":"pmbaccacabib14","first-page":"577","type":"other","article-title":"Patient-specific finetuning of deep learning models for adaptive radiotherapy in prostate CT","author":"Elmahdy","year":"2020"},{"key":"pmbaccacabib15","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.phro.2022.06.001","type":"journal-article","article-title":"Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy","volume":"23","author":"Fransson","year":"2022","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"pmbaccacabib16","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab843e","type":"journal-article","article-title":"Deep learning in medical image registration: a review","volume":"65","author":"Fu","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib17","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1088\/0031-9155\/55\/1\/012","type":"journal-article","article-title":"Implementation and evaluation of various demons deformable image registration algorithms on a GPU","volume":"55","author":"Gu","year":"2010","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib18","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s00138-020-01060-x","type":"journal-article","article-title":"Deep learning in medical image registration: a survey","volume":"31","author":"Haskins","year":"2020","journal-title":"Mach. Vis. Appl."},{"key":"pmbaccacabib19","doi-asserted-by":"publisher","DOI":"10.5281\/ZENODO.3632567","type":"journal-article","article-title":"batchgenerators\u2014a python framework for data augmentation","author":"Isensee","year":"2020"},{"key":"pmbaccacabib20","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.6.064003","type":"journal-article","article-title":"Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification","volume":"7","author":"Jansen","year":"2020","journal-title":"J. Med. Imaging"},{"key":"pmbaccacabib21","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.radonc.2015.11.004","type":"journal-article","article-title":"Geometric uncertainties in voluntary deep inspiration breath hold radiotherapy for locally advanced lung cancer","volume":"118","author":"Josipovic","year":"2016","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib22","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","type":"journal-article","article-title":"Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration","volume":"46","author":"Klein","year":"2009","journal-title":"NeuroImage"},{"key":"pmbaccacabib23","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","type":"journal-article","article-title":"A toolbox for intensity-based medical image registration","volume":"29","author":"Klein","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccacabib24","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.radonc.2019.03.004","type":"journal-article","article-title":"Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer","volume":"135","author":"Kosmin","year":"2019","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib25","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.radonc.2021.11.036","type":"journal-article","article-title":"Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy","volume":"167","author":"Kumar","year":"2022","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib26","doi-asserted-by":"publisher","DOI":"10.1118\/1.4901409","type":"journal-article","article-title":"Deformable image registration based automatic CT-to- CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting","volume":"41","author":"Kumarasiri","year":"2014","journal-title":"Med. Phys."},{"key":"pmbaccacabib27","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1016\/j.ijrobp.2017.04.023","type":"journal-article","article-title":"Online adaptive radiation therapy","volume":"99","author":"Lim-Reinders","year":"2017","journal-title":"Int. J. Radiat. Oncol.*Biol.*Phys."},{"key":"pmbaccacabib28","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1118\/1.3679340","type":"journal-article","article-title":"Robust optimization of intensity modulated proton therapy","volume":"39","author":"Liu","year":"2012","journal-title":"Med. Phys."},{"key":"pmbaccacabib29","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.717039","type":"journal-article","article-title":"Review of deep learning based automatic segmentation for lung cancer radiotherapy","volume":"11","author":"Liu","year":"2021","journal-title":"Front. Oncol."},{"key":"pmbaccacabib30","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1088\/0031-9155\/44\/1\/014","type":"journal-article","article-title":"Intensity modulation methods for proton radiotherapy","volume":"44","author":"Lomax","year":"1999","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib31","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1088\/0031-9155\/53\/4\/015","type":"journal-article","article-title":"Intensity modulated proton therapy and its sensitivity to treatment uncertainties: II. The potential effects of inter-fraction and inter-field motions","volume":"53","author":"Lomax","year":"2008","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib32","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.3109\/0284186X.2013.813069","type":"journal-article","article-title":"Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark?","volume":"52","author":"Mattiucci","year":"2013","journal-title":"Acta Oncol."},{"key":"pmbaccacabib33","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.oraloncology.2018.11.015","type":"journal-article","article-title":"Intensity modulated proton therapy (IMPT)\u2014the future of IMRT for head and neck cancer","volume":"88","author":"Moreno","year":"2019","journal-title":"Oral Oncol."},{"key":"pmbaccacabib34","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.radonc.2020.04.046","type":"journal-article","article-title":"Deformable image registration uncertainty for inter-fractional dose accumulation of lung cancer proton therapy","volume":"147","author":"Nenoff","year":"2020","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib35","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.radonc.2021.03.021","type":"journal-article","article-title":"Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients","volume":"159","author":"Nenoff","year":"2021","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib36","doi-asserted-by":"publisher","first-page":"e26151","DOI":"10.2196\/26151","type":"journal-article","article-title":"Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy","volume":"23","author":"Nikolov","year":"2021","journal-title":"Journal of Medical Imaging Research"},{"key":"pmbaccacabib37","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3857\/roj.2017.00325","type":"journal-article","article-title":"Deformable image registration in radiation therapy","volume":"35","author":"Oh","year":"2017","journal-title":"Radiat Oncol J."},{"key":"pmbaccacabib38","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1118\/1.2818738","type":"journal-article","article-title":"Volumetric modulated arc therapy: IMRT in a single gantry arc","volume":"35","author":"Otto","year":"2008","journal-title":"Med. Phys."},{"key":"pmbaccacabib39","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac344f","type":"journal-article","article-title":"Adaptive proton therapy","volume":"66","author":"Paganetti","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib40","doi-asserted-by":"crossref","DOI":"10.5244\/C.31.167","type":"conference-proceedings","article-title":"One-shot learning for semantic segmentation","author":"Shaban","year":"2017"},{"key":"pmbaccacabib41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2021.103865","type":"journal-article","article-title":"A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study","volume":"115","author":"Shaheen","year":"2021","journal-title":"J. Dentistry"},{"key":"pmbaccacabib42","article-title":"Plastimatch: an open source software suite for radiotherapy image processing","author":"Sharp","year":"2010","type":"other"},{"key":"pmbaccacabib43","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.radonc.2015.05.012","type":"journal-article","article-title":"Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: a multi-institution clinical study","volume":"115","author":"Tao","year":"2015","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib44","doi-asserted-by":"publisher","first-page":"918","DOI":"10.3109\/0284186X.2011.577806","type":"journal-article","article-title":"Deformable image registration for contour propagation from CT to cone-beam CT scans in radiotherapy of prostate cancer","volume":"50","author":"Thor","year":"2011","journal-title":"Acta Oncol."},{"key":"pmbaccacabib45","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13014-016-0761-0","type":"journal-article","article-title":"Treatment planning comparison of IMPT, VMAT and 4p radiotherapy for prostate cases","volume":"12","author":"Tran","year":"2017","journal-title":"Radiat. Oncol."},{"key":"pmbaccacabib46","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aae659","type":"journal-article","article-title":"Robust radiotherapy planning","volume":"63","author":"Unkelbach","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib47","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.radonc.2019.05.010","type":"journal-article","article-title":"Benefits of deep learning for delineation of organs at risk in head and neck cancer","volume":"138","author":"van der Veen","year":"2019","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib48","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1186\/s13014-020-01677-2","type":"journal-article","article-title":"Interobserver variability in organ at risk delineation in head and neck cancer","volume":"16","author":"van der Veen","year":"2021","journal-title":"Radiat. Oncol."},{"key":"pmbaccacabib49","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.radonc.2014.01.027","type":"journal-article","article-title":"CT-based patient modeling for head and neck hyperthermia treatment planning: Manual versus automatic normal-tissuesegmentation","volume":"111","author":"Verhaart","year":"2014","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib50","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6","type":"journal-article","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"pmbaccacabib51","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1118\/1.4894702","type":"journal-article","article-title":"The ANACONDA algorithm for deformable image registration in radiotherapy","volume":"42","author":"Weistrand","year":"2015","journal-title":"Med. Phys."},{"key":"pmbaccacabib52","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.radonc.2019.10.019","type":"journal-article","article-title":"Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning","volume":"144","author":"Wong","year":"2020","journal-title":"Radiother. Oncol."},{"key":"pmbaccacabib53","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.radmp.2020.11.002","type":"journal-article","article-title":"A review on 3D deformable image registration and its application in dose warping","volume":"1","author":"Xiao","year":"2020","journal-title":"Radiat. Med. Prot."},{"key":"pmbaccacabib54","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1088\/0031-9155\/42\/1\/008","type":"journal-article","article-title":"Adaptive radiation therapy","volume":"42","author":"Yan","year":"1997","journal-title":"Phys. Med. Biol."},{"key":"pmbaccacabib55","doi-asserted-by":"publisher","first-page":"4568","DOI":"10.1002\/mp.13141","type":"journal-article","article-title":"Autosegmentation for thoracic radiation treatment planning: a grand challenge at AAPM 2017","volume":"45","author":"Yang","year":"2018","journal-title":"Med. Phys."},{"key":"pmbaccacabib56","doi-asserted-by":"publisher","first-page":"e401","DOI":"10.1016\/j.ijrobp.2022.07.1568","type":"journal-article","article-title":"Reduction of inter-observer variability using mri and ct fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis","volume":"114","author":"Zhang","year":"2022","journal-title":"Int. J. Radiat. Oncol.*Biol.*Phys."},{"key":"pmbaccacabib57","doi-asserted-by":"publisher","first-page":"4947","DOI":"10.1088\/0031-9155\/56\/15\/019","type":"journal-article","article-title":"Introducing an on-line adaptive procedure for prostate image guided intensity modulate proton therapy","volume":"56","author":"Zhang","year":"2011","journal-title":"Phys. Med. Biol."}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:10:34Z","timestamp":1778775034000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accaca"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,25]]},"references-count":57,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,4,25]]},"published-print":{"date-parts":[[2023,5,7]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/accaca","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,25]]},"assertion":[{"value":"Patient-specific neural networks for contour propagation in online adaptive radiotherapy","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2023 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2022-12-21","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-04-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-04-25","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}