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Further exploration is needed to improve image segmentation for personalized thyroid phantoms, assess uncertainty in\n                      <jats:italic>S<\/jats:italic>\n                      value calculations, and compare dose estimates between the MIRD schema and the Marinelli\u2013Quimby formula.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This work aims to design a multi\u2010scale deep learning model to enhance accuracy and reliability in absorbed dose calculations for hyperthyroid patients based on the MIRD schema.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      To improve multi\u2010scale semantic learning and integration, multi\u2010scale residual and multi\u2010head attention modules were added to the UNet++ architecture. An information entropy\u2010based uncertainty quantification module was proposed to assess segmentation confidence and enhance reliability. Patient\u2010specific thyroid voxel phantoms were created from clinical segmentation results,\n                      <jats:italic>S<\/jats:italic>\n                      values were calculated, and overall uncertainty was evaluated. Finally, absorbed dose differences between the MIRD schema and Marinelli\u2013Quimby formula were compared.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The comparative experiments on the publicly available head and neck organ\u2010at\u2010risk CT and MR segmentation (HaN\u2010Seg) dataset showed that the MSRA\u2010UNet++ model excelled in segmenting multiple organs. Specifically, for thyroid segmentation, the proposed model achieved a Dice Similarity Coefficient (DSC) of 88.23% and a Hausdorff Distance 95% (HD\n                      <jats:sub>95<\/jats:sub>\n                      ) of 9.43 pixels. When applied on clinical dataset, Jaccard Index (JI) improved 6.17%, compared to UNet++. The uncertainty quantification results showed higher uncertainty at the thyroid edges and low uncertainty in the interior. Patient\u2010specific phantoms were created from segmentation results, and the\n                      <jats:italic>S<\/jats:italic>\n                      value was calculated using Monte Carlo method. Time\u2010integral activity was determined by radioiodine kinetics data. The absorbed dose was evaluated using the MIRD schema and compared with the Marinelli\u2013Quimby formula, which overestimated the MIRD schema by an average of 7.74%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The proposed MSRA\u2010UNet++ network provides segmentation results with estimated uncertainty and higher accuracy. The reliability of\n                      <jats:italic>S<\/jats:italic>\n                      values was enhanced by combining segmentation uncertainty with Monte Carlo simulations. To the best of our knowledge, this study is the first to introduce an uncertainty quantification module into the CT image segmentation process for hyperthyroid patients. Additionally, it combines patient\u2010specific thyroid voxel phantoms with individualized radioiodine kinetics data to assess the absorbed dose using the MIRD schema, with the results compared to the Marinelli\u2013Quimby formula. The code will be release on\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/410435553\">https:\/\/github.com\/410435553<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70072","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T03:19:16Z","timestamp":1761189556000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A multi\u2010scale network for thyroid segmentation and dose assessment in hyperthyroid patients with uncertainty quantification"],"prefix":"10.1002","volume":"52","author":[{"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhi","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqiang","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huasi","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Xi'an Jiaotong University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2021.0302"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2016.0229"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.108.056036"},{"issue":"2","key":"e_1_2_9_5_1","first-page":"260","article-title":"Dosage determination with radioactive isotopes; practical considerations in therapy and protection","volume":"59","author":"Marinelli LD","year":"1948","journal-title":"Am J Radium Ther"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365\u20102362.1995.tb01547.x"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2210564"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259\u2010010\u20101536\u20108"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12020\u2010016\u20101074\u20107"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278\u2010019\u201000227\u2010x"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462\u2010020\u201009854\u20101"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12123064"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17244"},{"key":"e_1_2_9_14_1","unstructured":"VaswaniA ShazeerN ParmarN et\u00a0al.Attention is all you need. 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