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However, accurately segmenting the gross tumor volume (GTV), which includes both the primary\u00a0tumor (GTVp) and lymph nodes (GTVn), remains challenging. Recently,\u00a0two deep learning segmentation innovations have shown great promise: UMamba, which effectively captures long-range dependencies, and\u00a0the nnU-Net Residual Encoder (ResEnc), which enhances feature extraction through multistage residual blocks. In this study, we integrate these strengths into a novel approach, termed \u2018UMambaAdj\u2019.\u00a0Our proposed method was evaluated on the HNTS-MRG 2024 challenge\u00a0test set using pre-RT T2-weighted MRI images, achieving an aggregated Dice Similarity Coefficient (<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\hbox {DSC}_{agg}$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mtext>DSC<\/mml:mtext>\n                    <mml:mrow>\n                      <mml:mi>agg<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msub>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>) of 0.751 for GTVp\u00a0and 0.842 for GTVn, with a mean <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\hbox {DSC}_{agg}$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mtext>DSC<\/mml:mtext>\n                    <mml:mrow>\n                      <mml:mi>agg<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msub>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> of 0.796. This approach demonstrates potential for more precise tumor delineation\u00a0in MRI-guided adaptive radiotherapy, ultimately improving treatment outcomes for HNC patients. Team: DCPT-Stine\u2019s group.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_9","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:44Z","timestamp":1740919364000},"page":"123-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["UMamba Adjustment: Advancing GTV Segmentation for\u00a0Head and\u00a0Neck Cancer in\u00a0MRI-Guided RT with\u00a0UMamba and\u00a0NnU-Net ResEnc Planner"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1558-7196","authenticated-orcid":false,"given":"Jintao","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7347-1688","authenticated-orcid":false,"given":"Kim","family":"Hochreuter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3705-5390","authenticated-orcid":false,"given":"Jesper Folsted","family":"Kallehauge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3523-382X","authenticated-orcid":false,"given":"Stine Sofia","family":"Korreman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.radonc.2009.12.021","volume":"94","author":"M Ahmed","year":"2010","unstructured":"Ahmed, M., et al.: The value of magnetic resonance imaging in target volume delineation of base of tongue tumours-a study using flexible surface coils. 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