{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T01:45:44Z","timestamp":1791855944492,"version":"4.3.4"},"reference-count":38,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007194","name":"Wenzhou Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["Y20190183"],"award-info":[{"award-number":["Y20190183"]}],"id":[{"id":"10.13039\/501100007194","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>An accurate and reliable target volume delineation is critical for the safe and successful radiotherapy. The purpose of this study is to develop new 2D and 3D automatic segmentation models based on RefineNet for clinical target volume (CTV) and organs at risk (OARs) for postoperative cervical cancer based on computed tomography (CT) images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A 2D RefineNet and 3D RefineNetPlus3D were adapted and built to automatically segment CTVs and OARs on a total of 44\u2009222 CT slices of 313 patients with stage I\u2013III cervical cancer. Fully convolutional networks (FCNs), U\u2010Net, context encoder network (CE\u2010Net), UNet3D, and ResUNet3D were also trained and tested with randomly divided training and validation sets, respectively. The performances of these automatic segmentation models were evaluated by Dice similarity coefficient (DSC), Jaccard similarity coefficient, and average symmetric surface distance when comparing them with manual segmentations with the test data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The DSC for RefineNet, FCN, U\u2010Net, CE\u2010Net, UNet3D, ResUNet3D, and RefineNet3D were 0.82, 0.80, 0.82, 0.81, 0.80, 0.81, and 0.82 with a mean contouring time of 3.2, 3.4, 8.2, 3.9, 9.8, 11.4, and 6.4\u00a0s, respectively. The generated RefineNetPlus3D demonstrated a good performance in the automatic segmentation of bladder, small intestine, rectum, right and left femoral heads with a DSC of 0.97, 0.95, 091, 0.98, and 0.98, respectively, with a mean computation time of 6.6\u00a0s.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The newly adapted RefineNet and developed RefineNetPlus3D were promising automatic segmentation models with accurate and clinically acceptable CTV and OARs for cervical cancer patients in postoperative radiotherapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13631","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T17:49:27Z","timestamp":1652118567000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["RefineNet\u2010based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy"],"prefix":"10.1002","volume":"23","author":[{"given":"Chengjian","family":"Xiao","sequence":"first","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juebin","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinling","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ce","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Ai","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congying","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"},{"name":"Department of Radiation and Medical Oncology Wenzhou Medical University Second Affiliated Hospital  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiance","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Center Wenzhou Medical University First Affiliated Hospital  Wenzhou People's Republic of China"},{"name":"School of Basic Medical Science Wenzhou Medical University  Wenzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.20107"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-3016(01)01664-9"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12003"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2010.0106"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.10.075"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.10.007"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.001"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/0-306-48608-3_11"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.03.004"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2009.08.013"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2011.06.032"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/0284186X.2017.1285499"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2519264"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"OsmanAFI.Automated brain tumor segmentation on magnetic resonance images and patient's overall survival prediction using support vector machines. In:Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). Vol.\u00a010670 LNCS.2018:435\u2010449.","DOI":"10.1007\/978-3-319-75238-9_37"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2508280"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12602"},{"key":"e_1_2_9_18_1","unstructured":"BnouniN AmorHB RekikI RhimMS SolaimanB AmaraNEB.Boosting CNN learning by ensemble image preprocessing methods for cervical cancer MR image segmentation. In:Int Conf Sensors Syst Signals Adv Technol.2018."},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.09.060"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1049\/el.2020.0453"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2017.2695896"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2007.09.042"},{"key":"e_1_2_9_23_1","unstructured":"NekrasovV ShenC ReidI.Light\u2010Weight RefineNet for Real\u2010Time Semantic Segmentation.2018 arXiv 10.48550\/arXiv.1810.03272."},{"key":"e_1_2_9_24_1","doi-asserted-by":"crossref","unstructured":"LinG MilanA ShenC ReidI.RefineNet: Multi\u2010path Refinement Networks for High\u2010Resolution Semantic Segmentation. IEEE Computer Society;2017. doi:10.1109\/CVPR.2017.549","DOI":"10.1109\/CVPR.2017.549"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010Net: convolutional networks for biomedical image segmentation. In:International Conference on Medical Image Computing and Computer\u2010Assisted Intervention. Vol.\u00a09351.2015:234\u2010241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903562"},{"key":"e_1_2_9_28_1","volume-title":"3D U\u2010Net: Learning Dense Volumetric Segmentation from Sparse Annotation","author":"\u00c7i\u00e7ek \u00d6","year":"2016"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.01.020"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.03.068"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3002417"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.02.006"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-020-07595-6"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14467"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13097"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12045"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2019.11.006"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaf11c"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2016.01.006"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.13631","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/acm2.13631","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.13631","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T01:15:49Z","timestamp":1692839749000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.13631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,9]]},"references-count":38,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["10.1002\/acm2.13631"],"URL":"https:\/\/doi.org\/10.1002\/acm2.13631","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,9]]},"assertion":[{"value":"2021-12-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-18","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e13631"}}