{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T20:54:59Z","timestamp":1789160099121,"version":"build-2803163510"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["phiro.science","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Physics and Imaging in Radiation Oncology"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.phro.2022.06.001","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T12:56:19Z","timestamp":1654260979000},"page":"38-42","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":26,"special_numbering":"C","title":["Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy"],"prefix":"10.1016","volume":"23","author":[{"given":"Samuel","family":"Fransson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Tilly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Strand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.phro.2022.06.001_b0005","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.radonc.2018.12.011","article-title":"MRI commissioning of 1.5T MR-linac systems \u2013 a multi-institutional study","volume":"132","author":"Tijssen","year":"2019","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0010","article-title":"Online adaptive radiation therapy: implementation of a new process of care","volume":"9","author":"Lamb","year":"2017","journal-title":"Cureus"},{"key":"10.1016\/j.phro.2022.06.001_b0015","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1080\/0284186X.2019.1620331","article-title":"Deformable image registration for radiation therapy: principle, methods, applications and evaluation","volume":"58","author":"Rigaud","year":"2019","journal-title":"Acta Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0020","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1002\/mp.12737","article-title":"Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study","volume":"45","author":"Loi","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2022.06.001_b0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1748-717X-7-160","article-title":"Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer","volume":"7","author":"La Macchia","year":"2012","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0030","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.3109\/0284186X.2010.503662","article-title":"Propagation of target and organ at risk contours in radiotherapy of prostate cancer using deformable image registration","volume":"49","author":"Th\u00f6rnqvist","year":"2010","journal-title":"Acta Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0035","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1002\/mp.13620","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":"10.1016\/j.phro.2022.06.001_b0040","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/j.adro.2020.04.027","article-title":"A patient-specific autosegmentation strategy using multi-input deformable image registration for magnetic resonance imaging-guided online adaptive radiation therapy: a feasibility study","volume":"5","author":"Zhang","year":"2020","journal-title":"Adv Radiat Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0045","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1002\/mp.13994","article-title":"Fast contour propagation for MR-guided prostate radiotherapy using convolutional neural networks","volume":"47","author":"Eppenhof","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2022.06.001_b0050","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.phro.2022.02.008","article-title":"Fast and accurate deformable contour propagation for intra-fraction adaptive magnetic resonance-guided prostate radiotherapy","volume":"21","author":"Willigenburg","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0055","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep learning techniques for medical image segmentation: achievements and challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"J Digit Imaging"},{"key":"10.1016\/j.phro.2022.06.001_b0060","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009","journal-title":"IEEE Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.phro.2022.06.001_b0065","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TUFFC.2020.3015081","article-title":"Fine-tuning U-net for ultrasound image segmentation: different layers, different outcomes","volume":"67","author":"Amiri","year":"2020","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"key":"10.1016\/j.phro.2022.06.001_b0070","unstructured":"Karimi D, Warfield SK, Gholipour A. Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks. arXiv:2006.00356 [cs.CV] 2020;1\u201311."},{"key":"10.1016\/j.phro.2022.06.001_b0075","first-page":"577","article-title":"Patient-specific finetuning of deep learning models for adaptive radiotherapy in prostate CT","author":"Elmahdy","year":"2020","journal-title":"Proc - Int Symp Biomed Imaging"},{"key":"10.1016\/j.phro.2022.06.001_b0080","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JMI.7.6.064003","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":"10.1016\/j.phro.2022.06.001_b0085","series-title":"NIPS\u201917 Proc 31st Int Conf Neural Inf Process Syst","first-page":"972","article-title":"Self-Normalizing Neural Networks","author":"Klambauer","year":"2017"},{"key":"10.1016\/j.phro.2022.06.001_b0090","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.phro.2022.06.001_b0095","unstructured":"Xie J, Xu B, Chuang Z. Horizontal and Vertical Ensemble with Deep Representation for Classification. arXiv:1306.2759 [cs.LG] 2013."},{"key":"10.1016\/j.phro.2022.06.001_b0100","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1002\/mp.13200","article-title":"Comparative evaluation of autocontouring in clinical practice: a practical method using the Turing test","volume":"45","author":"Gooding","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2022.06.001_b0105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.phro.2019.12.001","article-title":"Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy","volume":"13","author":"Vaassen","year":"2020","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2022.06.001_b0110","doi-asserted-by":"crossref","unstructured":"Shivdeo A, Lokwani R, Kulkarni V, Kharat A, Pant A. Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans. arXiv:2101.07612 [eess.IV] 2021.","DOI":"10.1109\/icABCD51485.2021.9519338"},{"key":"10.1016\/j.phro.2022.06.001_b0115","series-title":"Int Conf Electron Information, Commun ICEIC 2021","article-title":"Comparison of tissue segmentation performance between 2D U-Net and 3D U-Net on brain MR Images","author":"Woo","year":"2021"},{"key":"10.1016\/j.phro.2022.06.001_b0120","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics10110959","article-title":"A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-a retrospective multicenter study","volume":"10","author":"Kiljunen","year":"2020","journal-title":"Diagnostics"},{"key":"10.1016\/j.phro.2022.06.001_b0125","series-title":"4th Int Conf 3D Vis","first-page":"565","article-title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.phro.2022.06.001_b0130","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1002\/mp.13765","article-title":"Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm","volume":"46","author":"Wang","year":"2019","journal-title":"Med Phys"}],"container-title":["Physics and Imaging in Radiation Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631622000495?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631622000495?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T08:14:28Z","timestamp":1762503268000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2405631622000495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":26,"alternative-id":["S2405631622000495"],"URL":"https:\/\/doi.org\/10.1016\/j.phro.2022.06.001","relation":{},"ISSN":["2405-6316"],"issn-type":[{"value":"2405-6316","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy","name":"articletitle","label":"Article Title"},{"value":"Physics and Imaging in Radiation Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.phro.2022.06.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.","name":"copyright","label":"Copyright"}]}}