{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:59:53Z","timestamp":1755223193647,"version":"3.43.0"},"reference-count":31,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102021R1A2C2008695","NRF\u20102022R1A2C1010337"],"award-info":[{"award-number":["NRF\u20102021R1A2C2008695","NRF\u20102022R1A2C1010337"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019266","name":"Korea Medical Device Development Fund","doi-asserted-by":"publisher","award":["1711196423","RS\u20102023\u201000254868"],"award-info":[{"award-number":["1711196423","RS\u20102023\u201000254868"]}],"id":[{"id":"10.13039\/100019266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0019304"],"award-info":[{"award-number":["P0019304"]}],"id":[{"id":"10.13039\/501100003661","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":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Although generalized\u2010dataset\u2010based auto\u2010segmentation models that consider various computed tomography (CT) scanners have shown great clinical potential, their application to medical images from unseen scanners remains challenging because of device\u2010dependent image features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aims to investigate the performance of a device\u2010dependent auto\u2010segmentation model based on a combined dataset of a generalized dataset and single CT scanner dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>We constructed two training datasets for 21 chest and abdominal organs. The generalized dataset comprised 1203 publicly available multi\u2010scanner data. The device\u2010dependent dataset comprised 1253 data, including the 1203 multi\u2010CT scanner data and 50 single CT scanner data. Using these datasets, the generalized\u2010dataset\u2010based model (GDSM) and the device\u2010dependent\u2010dataset\u2010based model (DDSM) were trained. The models were trained using nnU\u2010Net and tested on ten data samples from a single CT scanner. The evaluation metrics included the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD), which were used to assess the overall performance of the models. In addition, DSC<jats:sub>diff<\/jats:sub>, HD<jats:sub>ratio<\/jats:sub>, and ASSD<jats:sub>ratio<\/jats:sub>, which are variations of the three existing metrics, were used to compare the performance of the models across different organs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Result<\/jats:title><jats:p>For the average DSC, the GDSM and DDSM had values of 0.9251 and 0.9323, respectively. For the average HD, the GDSM and DDSM had values of 10.66 and 9.139\u00a0mm, respectively; for the average ASSD, the GDSM and DDSM had values of 0.8318 and 0.6656\u00a0mm, respectively. Compared with the GDSM, the DDSM showed consistent performance improvements of 0.78%, 14%, and 20% for the DSC, HD, and ASSD metrics, respectively. In addition, compared with the GDSM, the DDSM had better DSC<jats:sub>diff<\/jats:sub> values in 14 of 21 tested organs, better HD<jats:sub>ratio<\/jats:sub> values in 13 of 21 tested organs, and better ASSD<jats:sub>ratio<\/jats:sub> values in 14 of 21 tested organs. The three averages of the variant metrics were all better for the DDSM than for the GDSM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The results suggest that combining the generalized dataset with a single scanner dataset resulted in an overall improvement in model performance for that device image.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17570","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T07:45:50Z","timestamp":1734594350000},"page":"2375-2383","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A device\u2010dependent auto\u2010segmentation method based on combined generalized and single\u2010device datasets"],"prefix":"10.1002","volume":"52","author":[{"given":"Hyeongjin","family":"Lim","sequence":"first","affiliation":[{"name":"Department of Bio\u2010medical Engineering Korea University  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongha","family":"Gi","sequence":"additional","affiliation":[{"name":"Department of Bio\u2010medical Engineering Korea University  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yousun","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Bio\u2010medical Engineering Korea University  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhui","family":"Jo","sequence":"additional","affiliation":[{"name":"Institute of Global Health Technology (IGHT) Korea University  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyoung","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Bio\u2010medical Engineering Korea University  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghyun","family":"Kim","sequence":"additional","affiliation":[{"name":"FieldCure Ltd  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Hwan","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of\u00a0Radiation Oncology Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hee\u2010Chul","family":"Park","sequence":"additional","affiliation":[{"name":"Department of\u00a0Radiation Oncology Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haeyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of\u00a0Radiation Oncology Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwangzoo","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of\u00a0Radiation Oncology Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myonggeun","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Bio\u2010medical Engineering Korea University  Seoul Republic of Korea"},{"name":"FieldCure Ltd  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.4103\/0971-6203.58777"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.04.005"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.carj.2010.02.007"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"SevillaJ HeimL HoA BesirogluT HobbhahnM VillalobosP.Compute trends across three eras of machine learning.2022 International Joint Conference on Neural Networks (IJCNN).IEEE.2022:1\u20108.","DOI":"10.1109\/IJCNN55064.2022.9891914"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12194-017-0406-5"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation.Medical Image Computing and Computer\u2010Assisted Intervention\u2013MICCAI 2015: 18th International Conference Munich Germany October 5\u20139 2015 Proceedings Part III 18.Springer International Publishing.2015:234\u2010241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"7","key":"e_1_2_8_8_1","first-page":"3523","article-title":"Image segmentation using deep learning: a survey","volume":"44","author":"Minaee S","year":"2021","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"XiaoX LianS LuoZ LiS.Weighted res\u2010unet for high\u2010quality retina vessel segmentation.2018 9th international conference on information technology in medicine and education (ITME).IEEE.2018:327\u2010331.","DOI":"10.1109\/ITME.2018.00080"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek\u00d6 AbdulkadirA LienkampSS BroxT RonnebergerO.3D U\u2010Net: learning dense volumetric segmentation from sparse annotation.Medical Image Computing and Computer\u2010Assisted Intervention\u2013MICCAI 2016: 19th International Conference Athens Greece October 17\u201321 2016 Proceedings Part II 19.Springer International Publishing.2016:424\u2010432.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_2_8_13_1","first-page":"3","volume-title":"Unet++: a Nested u\u2010net Architecture for Medical Image Segmentation","author":"Zhou Z","year":"2018"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2020.605132"},{"key":"e_1_2_8_15_1","first-page":"272","volume-title":"International MICCAI Brainlesion Workshop","author":"Hatamizadeh A","year":"2021"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"GhafoorianM MehrtashA KapurT et al.Transfer learning for domain adaptation in MRI: application in brain lesion segmentation.Medical Image Computing and Computer Assisted Intervention\u2212 MICCAI 2017: 20th International Conference Quebec City QC Canada September 11\u201313 2017 Proceedings Part III 20.Springer International Publishing;2017:516\u2010524.","DOI":"10.1007\/978-3-319-66179-7_59"},{"key":"e_1_2_8_19_1","unstructured":"SelimM ZhangJ FeiB ZhangGQ ChenJ.STAN\u2010CT: standardizing CT image using generative adversarial networks.AMIA Annual Symposium Proceedings.American Medical Informatics Association.2020:1100."},{"key":"e_1_2_8_20_1","first-page":"826","article-title":"Understanding sources of variation to improve the reproducibility of radiomics","volume":"11","author":"Zhao B","year":"2021","journal-title":"Front Oncol"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13763"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4752209"},{"volume-title":"Deep Learning","year":"2016","author":"Goodfellow I","key":"e_1_2_8_23_1"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190195"},{"key":"e_1_2_8_25_1","doi-asserted-by":"crossref","unstructured":"FullPM IsenseeF J\u00e4gerPF Maier\u2010HeinK.Studying robustness of semantic segmentation under domain shift in cardiac MRI.In Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop STACOM 2020 Held in Conjunction with MICCAI 2020 Lima Peru October 4 2020 Revised Selected Papers 11.Springer International Publishing;2021:238\u2010249.","DOI":"10.1007\/978-3-030-68107-4_24"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.1.015006"},{"key":"e_1_2_8_27_1","doi-asserted-by":"crossref","unstructured":"SummersC DinneenMJ.Improved mixed\u2010example data augmentation.2019 IEEE winter conference on applications of computer vision (WACV).IEEE;2019:1262\u20101270.","DOI":"10.1109\/WACV.2019.00139"},{"key":"e_1_2_8_28_1","doi-asserted-by":"crossref","unstructured":"ZhaoA BalakrishnanG DurandF GuttagJV DalcaAV.Data augmentation using learned transformations for one\u2010shot medical image segmentation.Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition.2019:8543\u20108553.","DOI":"10.1109\/CVPR.2019.00874"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2538465"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29637-2"},{"key":"e_1_2_8_31_1","first-page":"36722","article-title":"Amos: a large\u2010scale abdominal multi\u2010organ benchmark for versatile medical image segmentation","volume":"35","author":"Ji Y","year":"2022","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.5.054001"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.17570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T12:29:44Z","timestamp":1754915384000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.17570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["10.1002\/mp.17570"],"URL":"https:\/\/doi.org\/10.1002\/mp.17570","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"type":"print","value":"0094-2405"},{"type":"electronic","value":"2473-4209"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"2024-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}