{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T03:23:17Z","timestamp":1789269797025,"version":"build-2803163510"},"reference-count":63,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The delineation of organs at risk (OARs) is fundamental to cone\u2010beam CT (CBCT)\u2010based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning\u2010based rapid multiorgan delineation method for use in CBCT\u2010guided adaptive pancreatic radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study. First, instead of directly segmenting organs on CBCT images, a pretrained cycle\u2010consistent generative adversarial network (cycleGAN) was applied to generating synthetic CT images given CBCT images. Second, an advanced deep learning model called mask\u2010scoring regional convolutional neural network (MS R\u2010CNN) was applied on those synthetic CT to detect the positions and shapes of multiple organs simultaneously for final segmentation. The OAR contours delineated by the proposed method were validated and compared with expert\u2010drawn contours for geometric agreement using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and residual mean square distance (RMS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across eight abdominal OARs including duodenum, large bowel, small bowel, left and right kidneys, liver, spinal cord, and stomach, the geometric comparisons between automated and expert contours are as follows: 0.92 (0.89\u20130.97) mean DSC, 2.90\u00a0mm (1.63\u20134.19\u00a0mm) mean HD95, 0.89\u00a0mm (0.61\u20131.36\u00a0mm) mean MSD, and 1.43\u00a0mm (0.90\u20132.10\u00a0mm) mean RMS. Compared to the competing methods, our proposed method had significant improvements (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) in all the metrics for all the eight organs. Once the model was trained, the contours of eight OARs can be obtained on the order of seconds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We demonstrated the feasibility of a synthetic CT\u2010aided deep learning framework for automated delineation of multiple OARs on CBCT. The proposed method could be implemented in the setting of pancreatic adaptive radiotherapy to rapidly contour OARs with high accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.15264","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T09:07:24Z","timestamp":1633597644000},"page":"7063-7073","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Synthetic CT\u2010aided multiorgan segmentation for CBCT\u2010guided adaptive pancreatic radiotherapy"],"prefix":"10.1002","volume":"48","author":[{"given":"Xianjin","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Wynne","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Janopaul\u2010Naylor","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonghe","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Roper","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter J.","family":"Curran","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pretesh","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2013.02.022"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.10.030"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1200\/jco.2008.26.15_suppl.4506"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12071729"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.10.015"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-019-1309-x"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420034110"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2818738"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1350587"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/57\/11\/R99"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.09.028"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2007.11.032"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.06.029"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2011.12.073"},{"key":"e_1_2_9_17_1","doi-asserted-by":"crossref","unstructured":"ChenI MittauerK HenkeL et\u00a0al.Quantification of interfractional gastrointestinal tract motion for pancreatic cancer radiation therapy.2016;96(2):E144.","DOI":"10.1016\/j.ijrobp.2016.06.954"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"De LangeS Van GroeningenC MeijerO et\u00a0al.Gemcitabine\u2013radiotherapy in patients with locally advanced pancreatic cancer.2002;38(9):1212\u20101217.","DOI":"10.1016\/S0959-8049(02)00076-X"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfaa021"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.05.013"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2013.11.003"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.04.023"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-019-1275-3"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.011"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1177\/1533034617697242"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meddos.2015.04.002"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13566"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa6419"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tipsro.2019.10.003"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101829"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12717"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0099-z"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.02.040"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12480"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018181432"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2806309"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.03.003"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.09.028"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14307"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.10.002"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.05.003"},{"key":"e_1_2_9_42_1","article-title":"Synthetic MRI\u2010aided head\u2010and\u2010neck organs\u2010at\u2010risk auto\u2010delineation for CBCT\u2010guided adaptive radiotherapy","author":"Dai X","year":"2020","journal-title":"arXiv"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.01333"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14539"},{"key":"e_1_2_9_45_1","unstructured":"WangT LeiY FuY CurranWJ LiuT YangX.Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review. Cornell University;2020."},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14196"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.07.2080"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab63bb"},{"key":"e_1_2_9_49_1","doi-asserted-by":"crossref","unstructured":"ZhuJ\u2010Y ParkT IsolaP EfrosAA.Unpaired image\u2010to\u2010image translation using cycle\u2010consistent adversarial networks. Paper presented at: Proceedings of the IEEE International Conference on Computer Vision;2017.","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_2_9_50_1","unstructured":"GoodfellowI Pouget\u2010AbadieJ MirzaM et\u00a0al.Generative adversarial nets. Paper presented at: Advances in Neural Information Processing Systems;2014."},{"key":"e_1_2_9_51_1","doi-asserted-by":"crossref","unstructured":"HeK GkioxariG Doll\u00e1rP GirshickR.Mask R\u2010CNN. Paper presented at: Proceedings of the IEEE International Conference on Computer Vision;2017.","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_2_9_52_1","doi-asserted-by":"publisher","DOI":"10.1177\/1536012119863531"},{"key":"e_1_2_9_53_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000015200"},{"key":"e_1_2_9_54_1","doi-asserted-by":"crossref","unstructured":"LiuM DongJ DongX YuH QiL.Segmentation of lung nodule in CT images based on mask R\u2010CNN. Paper presented at: 2018 9th International Conference on Awareness Science and Technology (iCAST);2018.","DOI":"10.1109\/ICAwST.2018.8517248"},{"key":"e_1_2_9_55_1","doi-asserted-by":"crossref","unstructured":"HuangZ HuangL GongY HuangC WangX.Mask scoring R\u2010CNN. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition;2019.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"e_1_2_9_56_1","doi-asserted-by":"crossref","unstructured":"LinT\u2010Y Doll\u00e1rP GirshickR HeK HariharanB BelongieS.Feature pyramid networks for object detection. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition;2017.","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_9_57_1","unstructured":"RenS HeK GirshickR SunJ.Faster R\u2010CNN: towards real\u2010time object detection with region proposal networks. Paper presented at: Advances in Neural Information Processing Systems;2015."},{"key":"e_1_2_9_58_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13577"},{"key":"e_1_2_9_59_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13656"},{"key":"e_1_2_9_60_1","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek\u00d6 AbdulkadirA LienkampSS BroxT RonnebergerO.3D U\u2010net: learning dense volumetric segmentation from sparse annotation. Paper presented at: International Conference on Medical Image Computing and Computer\u2010Assisted Intervention;2016.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_2_9_61_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abd953"},{"key":"e_1_2_9_62_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-018-1000-7"},{"key":"e_1_2_9_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.01.020"},{"key":"e_1_2_9_64_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14121"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.15264","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.15264","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.15264","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T08:17:56Z","timestamp":1736842676000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.15264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,13]]},"references-count":63,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10.1002\/mp.15264"],"URL":"https:\/\/doi.org\/10.1002\/mp.15264","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,13]]},"assertion":[{"value":"2020-12-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}