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By leveraging pre-RT images and their segmentations as prior knowledge, we address\u00a0the challenge of tumor localization in mid-RT segmentation. A gradient map of the tumor region from the pre-RT image is computed\u00a0and applied to mid-RT images to improve tumor boundary delineation.\u00a0Our approach demonstrated improved segmentation accuracy for\u00a0both primary GTV (GTVp) and nodal GTV (GTVn), though performance\u00a0was limited by data constraints. The final <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> scores from\u00a0the challenge\u2019s test set evaluation were 0.534 for GTVp, 0.867 for GTVn, and a mean score of 0.70. This method shows potential for enhancing segmentation and treatment planning in adaptive radiotherapy. Team: DCPT-Stine\u2019s group.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_2","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:30Z","timestamp":1740919350000},"page":"36-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gradient Map-Assisted Head and\u00a0Neck Tumor Segmentation: A Pre-RT to\u00a0Mid-RT Approach in\u00a0MRI-Guided Radiotherapy"],"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\/0000-0002-7853-3531","authenticated-orcid":false,"given":"Mathis Ersted","family":"Rasmussen","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":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Andrearczyk, V., et al.: Overview of the hecktor challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET\/CT images. 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