{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:13:44Z","timestamp":1745986424891,"version":"3.40.4"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031832734"},{"type":"electronic","value":"9783031832741"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":61,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Radiation therapy (RT) is essential in treating head\u00a0and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided\u00a0RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therefore remains a challenge. In this study, we present\u00a0our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which\u00a0is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid-RT MRI images. We utilized the HNTS-MRG2024 dataset,\u00a0which consists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn.\u00a0We employed two state-of-the-art models in deep learning, nnUNet\u00a0and MedNeXt. For Task 1, we pretrained models on pre-RT registered\u00a0and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we combined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input\u00a0for training. Our solution for <jats:bold>Task 1<\/jats:bold> achieved 1st place in\u00a0the final test phase with an aggregated Dice Similarity Coefficient\u00a0of <jats:bold>0.8254<\/jats:bold>, and our solution for <jats:bold>Task 2<\/jats:bold> ranked\u00a08th with a score of <jats:bold>0.7005<\/jats:bold>. The proposed solution is publicly available\u00a0at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/NikooMoradi\/HNTSMRG24_team_TUMOR\" ext-link-type=\"uri\">Github Repository<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_10","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:23Z","timestamp":1740919343000},"page":"136-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative Analysis of\u00a0nnUNet and\u00a0MedNeXt for\u00a0Head and\u00a0Neck Tumor Segmentation in\u00a0MRI-Guided Radiotherapy"],"prefix":"10.1007","author":[{"given":"Nikoo","family":"Moradi","sequence":"first","affiliation":[]},{"given":"Andr\u00e9","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Behrus","family":"Puladi","sequence":"additional","affiliation":[]},{"given":"Jens","family":"Kleesiek","sequence":"additional","affiliation":[]},{"given":"Emad","family":"Fatemizadeh","sequence":"additional","affiliation":[]},{"given":"Gijs","family":"Luijten","sequence":"additional","affiliation":[]},{"given":"Victor","family":"Alves","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Egger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"983","DOI":"10.3389\/fonc.2019.00983","volume":"9","author":"KJ Kiser","year":"2019","unstructured":"Kiser, K.J., Smith, B.D., Wang, J., Fuller, C.D.: Apr\u00e8s mois, le d\u00e9luge: preparing for the coming data flood in the MRI-guided radiotherapy era. 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