{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:29:37Z","timestamp":1768343377198,"version":"3.49.0"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82172072"],"award-info":[{"award-number":["82172072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020LZL001"],"award-info":[{"award-number":["ZR2020LZL001"]}],"id":[{"id":"10.13039\/501100007129","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":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Routinely delineating of important skeletal growth centers is imperative to mitigate radiation\u2010induced growth abnormalities for pediatric cancer patients treated with radiotherapy. However, it is hindered by several practical problems, including difficult identification, time consumption, and inter\u2010practitioner variability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The goal of this study was to construct and evaluate a novel Triplet\u2010Attention U\u2010Net (TAU\u2010Net)\u2010based auto\u2010segmentation model for important skeletal growth centers in childhood cancer radiotherapy, concentrating on the accuracy and time efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 107 childhood cancer patients fulfilled the eligibility criteria were enrolled in the training cohort (<jats:italic>N<\/jats:italic>\u00a0=\u00a080) and test cohort (<jats:italic>N<\/jats:italic>\u00a0=\u00a027). The craniofacial growth plates, shoulder growth centers, and pelvic ossification centers, with a total of 19 structures in the three groups, were manually delineated by two experienced radiation oncologists on axial, coronal, and sagittal computed tomography images. Modified from U\u2010Net, the proposed TAU\u2010Net has one main branch and two bypass branches, receiving semantic information of three adjacent slices to predict the target structure. With supervised deep learning, the skeletal growth centers contouring of each group was generated by three different auto\u2010segmentation models: U\u2010Net, V\u2010Net, and the proposed TAU\u2010Net. Dice similarity coefficient (DSC) and Hausdorff distance 95% (HD95) were used to evaluate the accuracy of three auto\u2010segmentation models. The time spent on performing manual tasks and manually correcting auto\u2010contouring generated by TAU\u2010Net was recorded. The paired <jats:italic>t<\/jats:italic>\u2010test was used to compare the statistical differences in delineation quality and time efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the three groups, including craniofacial growth plates, shoulder growth centers, and pelvic ossification centers groups, TAU\u2010Net had demonstrated highly acceptable performance (the average DSC\u00a0=\u00a00.77, 0.87, and 0.83 for each group; the average HD95\u00a0=\u00a02.28, 2.07, and 2.86\u00a0mm for each group). In the overall evaluation of 19 regions of interest (ROIs) in the test cohort, TAU\u2010Net had an overwhelming advantage over U\u2010Net (63.2% ROIs in DSC and 31.6% ROIs in HD95, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.001\u20130.042) and V\u2010Net (94.7% ROIs in DSC and 36.8% ROIs in HD95, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.001\u20130.040). With an average time of 52.2\u00a0min for manual delineation, the average time saved to adjust TAU\u2010Net\u2010generated contours was 37.6\u00a0min (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001), a 72% reduction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Deep learning\u2013based models have presented enormous potential for the auto\u2010segmentation of important growth centers in pediatric skeleton, where the proposed TAU\u2010Net outperformed the U\u2010Net and V\u2010Net in geometrical precision for the majority status.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15919","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T07:53:07Z","timestamp":1662018787000},"page":"284-296","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Auto\u2010segmentation of important centers of growth in the pediatric skeleton to consider during radiation therapy based on deep learning"],"prefix":"10.1002","volume":"50","author":[{"given":"Wenlong","family":"Qiu","sequence":"first","affiliation":[{"name":"Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"},{"name":"Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Manteia Technologies Co., Ltd  Xiamen P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingmin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youyong","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Southeast University  Nanjing P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyue","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Fu","sequence":"additional","affiliation":[{"name":"Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"},{"name":"Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dandan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. 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China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qichao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Manteia Technologies Co., Ltd  Xiamen P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. China"},{"name":"Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan P. R. 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