{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:08:02Z","timestamp":1787616482708,"version":"build-2736575974"},"reference-count":48,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2020,8,30]],"date-time":"2020-08-30T00:00:00Z","timestamp":1598745600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R21 CA234637"],"award-info":[{"award-number":["R21 CA234637"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R01 EB029088"],"award-info":[{"award-number":["R01 EB029088"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Segmentation of multiple organs\u2010at\u2010risk (OARs) is essential for magnetic resonance (MR)\u2010only radiation therapy treatment planning and MR\u2010guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor\u2010intensive, time\u2010consuming, and prone to intra\u2010 and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed Automated deep Learning\u2010based abdominal multiorgan segmentation (ALAMO) technique based on two\u2010dimensional U\u2010net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0\u2010Tesla T1 VIBE (Volumetric Interpolated Breath\u2010hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left\/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground\u2010truth. The performance was measured using volume overlapping and surface distance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87\u20130.96, except for the duodenum with a DSC of 0.80. The inference completed within 1\u00a0min for a three\u2010dimensional volume of 320\u00a0\u00d7\u00a0288\u00a0\u00d7\u00a0180. Overall, the ALAMO model matched the state\u2010of\u2010the\u2010art techniques in performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14429","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T07:38:03Z","timestamp":1596526683000},"page":"4971-4982","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks"],"prefix":"10.1002","volume":"47","author":[{"given":"Yuhua","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Bioengineering University of California Los Angeles CA USA"},{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Bioengineering University of California Los Angeles CA USA"},{"name":"Department of Radiation Oncology University of California Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixia","family":"Wang","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center Los Angeles CA USA"},{"name":"Department of Radiology Chaoyang Hospital Capital Medical University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiology Fujian Medical University Union Hospital Fuzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rola","family":"Saouaf","sequence":"additional","affiliation":[{"name":"Department of Imaging Cedars\u2010Sinai Medical Center Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wensha","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Southern California Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debiao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Bioengineering University of California Los Angeles CA USA"},{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center Los Angeles CA USA"},{"name":"Department of Medicine University of California Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyang","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Bioengineering University of California Los Angeles CA USA"},{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center Los Angeles CA USA"},{"name":"Department of Medicine University of California Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,8,30]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4758068"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaaca4"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26271"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-8-183"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2016.10.006"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.02.035"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.07.006"},{"key":"e_1_2_8_9_1","unstructured":"KohlbergerT SofkaM ZhangJ et al.Automatic multi\u2010organ segmentation using learning\u2010based segmentation and level set optimization. Medical Image Computing and Computer\u2010Assisted Intervention. Lecture Notes in Computer Science 6893. Berlin Heidelberg: Springer.https:\/\/doi.org\/10.1007\/978\u20103\u2010642\u201023626\u20106_42;2011."},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(99)00019-7"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2011.12.001"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26534"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.06.012"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.11.012"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2782687"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693"},{"key":"e_1_2_8_18_1","unstructured":"KayalibayB JensenG van derSmagtP.CNN\u2010based segmentation of medical imaging data. arXiv:1701.03056;2017."},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-017-9983-4"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.01.005"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12079"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2806309"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.04.005"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13221"},{"key":"e_1_2_8_25_1","doi-asserted-by":"crossref","unstructured":"BoboMF BaoS HuoY et al.fully convolutional neural networks improve abdominal organ segmentation. Proc SPIE Int Soc. Opt Eng.2018;10574.https:\/\/doi.org\/10.1117\/12.2293751","DOI":"10.1117\/12.2293751"},{"key":"e_1_2_8_26_1","unstructured":"\u00c7i\u00e7ek\u00d6 AbdulkadirA LienkampSS et al.3D U\u2010Net: learning dense volumetric segmentation from sparse annotation. Medical Image Computing and Computer\u2010Assisted Intervention 2016. Lecture Notes in Computer Science 9901.Cham:Springer.https:\/\/doi.org\/10.1007\/978\u20103\u2010319\u201046723\u20108_49"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"e_1_2_8_28_1","unstructured":"HuangG LiuZ Van Der MaatenL et al.Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu HI pp.2261\u20132269.https:\/\/doi.org\/10.1109\/CVPR.2017.243;2017."},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.05.001"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.212.3.r99se34876"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015"},{"key":"e_1_2_8_32_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer\u2010assisted intervention. Springer;2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_8_33_1","first-page":"1","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons","volume":"5","author":"Sorensen TA","year":"1948","journal-title":"Biol Skar"},{"key":"e_1_2_8_34_1","first-page":"547","article-title":"\u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura","volume":"37","author":"Jaccard P","year":"1901","journal-title":"Bull Soc Vaudoise Sci Nat"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.232073"},{"key":"e_1_2_8_36_1","doi-asserted-by":"crossref","unstructured":"J\u00e9gouS DrozdzalM VazquezD et al.The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Honolulu HI pp.1175\u20131183.https:\/\/doi.org\/10.1109\/CVPRW.2017.156;2017","DOI":"10.1109\/CVPRW.2017.156"},{"key":"e_1_2_8_37_1","unstructured":"MastersD LuschiC.Revisiting small batch training for deep neural networks. arXiv:1804.07612;2018."},{"key":"e_1_2_8_38_1","unstructured":"UlyanovD VedaldiA LempitskyV.Instance normalization: The missing ingredient for fast stylization. arXiv:1607.08022;2016."},{"key":"e_1_2_8_39_1","unstructured":"BaJL KirosJR HintonGE.Layer normalization. arXiv:1607.06450;2016."},{"key":"e_1_2_8_40_1","unstructured":"RothHR LuL FaragA et al.Deeporgan: Multi\u2010level deep convolutional networks for automated pancreas segmentation. International conference on medical image computing and computer\u2010assisted intervention. Lecture Notes in Computer Science 9349.Cham:Springer.https:\/\/doi.org\/10.1007\/978\u20103\u2010319\u201024553\u20109_68;2015."},{"key":"e_1_2_8_41_1","unstructured":"AbadiM BarhamP ChenJ et al.Tensorflow: A system for large\u2010scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) USENIX Association pp.265\u2013283.2016."},{"key":"e_1_2_8_42_1","unstructured":"KingmaDP BaJ Adam: A method for stochastic optimization. arXiv:1412.6980;2014."},{"key":"e_1_2_8_43_1","unstructured":"HaqueH HashimotoM UetakeN et al.Semantic Segmentation of Thigh Muscle using 2.5 D Deep Learning Network Trained with Limited Datasets. arXiv:1911.0924;2019."},{"key":"e_1_2_8_44_1","doi-asserted-by":"crossref","unstructured":"Chen Y.XieY ZhouZ et al.Brain MRI super resolution using 3D deep densely connected neural networks. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) Washington DC pp.739\u2013742.https:\/\/doi.org\/10.1109\/ISBI.2018.8363679;2018.","DOI":"10.1109\/ISBI.2018.8363679"},{"key":"e_1_2_8_45_1","doi-asserted-by":"crossref","unstructured":"ChenY ShiF ChristodoulouAG et al.Efficient and accurate MRI super\u2010resolution using a generative adversarial network and 3D multi\u2010Level densely connected network. Medical Image Computing and Computer\u2010Assisted Intervention. Springer;2018.","DOI":"10.1007\/978-3-030-00928-1_11"},{"key":"e_1_2_8_46_1","doi-asserted-by":"crossref","unstructured":"CaiJ LuL ZhangZ et al.Pancreas segmentation in MRI using graph\u2010based decision fusion on convolutional neural networks. Medical Image Computing and Computer Assisted Intervention 2016;9901:442\u2013450.","DOI":"10.1007\/978-3-319-46723-8_51"},{"key":"e_1_2_8_47_1","unstructured":"CaiJ LuL XieY et al.Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. arXiv:1707.04912 2017."},{"key":"e_1_2_8_48_1","unstructured":"AsaturyanH ThomasEL FitzpatrickJ et al.Advancing Pancreas Segmentation in Multi\u2010protocol MRI Volumes Using Hausdorff\u2010Sine Loss Function. Machine Learning in Medical Imaging 2019. Lecture Notes in Computer Science 1861.Cham:Springer.https:\/\/doi.org\/10.1007\/978\u20103\u2010030\u201032692\u20100_4"},{"key":"e_1_2_8_49_1","doi-asserted-by":"publisher","DOI":"10.1177\/2058460119834690"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fmp.14429","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.14429","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.14429","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.14429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T08:37:01Z","timestamp":1745483821000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.14429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,30]]},"references-count":48,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["10.1002\/mp.14429"],"URL":"https:\/\/doi.org\/10.1002\/mp.14429","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,30]]},"assertion":[{"value":"2019-12-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-07-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-08-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}