{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:50:05Z","timestamp":1787064605562,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031832734","type":"print"},{"value":"9783031832741","type":"electronic"}],"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>\n                    Accurate segmentation of tumors in MRI-guided radiation therapy (RT) is crucial for effective treatment planning, particularly for complex malignancies such as head and neck cancer (HNC). This study presents a comparative analysis between\u00a0two state-of-the-art deep learning models, nnU-Net v2 and STU-Net,\u00a0for automatic tumor segmentation in pre-RT MRI images. While both models are designed for medical image segmentation, STU-Net introduces critical improvements in scalability and transferability,\u00a0with parameter sizes ranging from 14 million to 1.4 billion. Leveraging large-scale pre-training on datasets such as TotalSegmentator, STU-Net captures complex and variable tumor structures\u00a0more effectively. We modified the default nnU-Net v2 by adding additional convolutional layers to both the encoder and decoder, improving\u00a0its performance for MRI data. Based on our experimental results, STU-Net demonstrated better performance than nnU-Net v2 in the head and\u00a0neck tumor segmentation challenge. These findings suggest\u00a0that integrating advanced models like STU-Net into clinical workflows could remarkably enhance the precision of RT planning, potentially improving patient outcomes. Ultimately, the performance of\u00a0the fine-tuned STU-Net-B model submitted for the final evaluation\u00a0phase of Task 1 in this challenge achieved a DSCagg-GTVp of 0.76,\u00a0a DSCagg-GTVn of 0.85, and an overall DSCagg-mean score of 0.81, securing ninth place in the Task 1 rankings. The described solution is by team SZTU-SingularMatrix for Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 challenge. Link to\u00a0the trained model weights:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Duskwang\/Weight\/releases\" ext-link-type=\"uri\">https:\/\/github.com\/Duskwang\/Weight\/releases<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_4","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T07:42:44Z","timestamp":1740901364000},"page":"65-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Head and\u00a0Neck Tumor Segmentation for\u00a0MRI-Guided Radiation Therapy Using Pre-trained STU-Net Models"],"prefix":"10.1007","author":[{"given":"Zihao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengye","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"issue":"19","key":"4_CR1","doi-asserted-by":"publisher","first-page":"4912","DOI":"10.3390\/cancers13194912","volume":"13","author":"G Anderson","year":"2021","unstructured":"Anderson, G., Ebadi, M., Vo, K., Novak, J., Govindarajan, A., Amini, A.: An updated review on head and neck cancer treatment with radiation therapy. 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