{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T18:06:13Z","timestamp":1778177173752,"version":"3.51.4"},"publisher-location":"Cham","reference-count":19,"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>Head and neck tumors and metastatic lymph nodes\u00a0are crucial for treatment planning and prognostic analysis. Accurate segmentation and quantitative analysis of these structures require pixel-level annotation, making automated segmentation techniques essential for the diagnosis and treatment of head and neck cancer. In this study, we investigated the effects of multiple strategies\u00a0on the segmentation of pre-radiotherapy (pre-RT) and mid-radiotherapy (mid-RT) images. For the segmentation of pre-RT images, we utilized: 1) a fully supervised learning approach, and 2) the same approach enhanced with pre-trained weights and the MixUp data augmentation technique. For mid-RT images, we introduced a\u00a0novel computational-friendly network architecture that features separate encoders for mid-RT images and registered pre-RT images with\u00a0their labels. The mid-RT encoder branch integrates information from pre-RT images and labels progressively during the forward propagation.\u00a0We selected the highest-performing model from each fold and used\u00a0their predictions to create an ensemble average for inference. In\u00a0the final test, our models achieved a segmentation performance\u00a0of 82.38% for pre-RT and 72.53% for mid-RT on aggregated\u00a0Dice Similarity Coefficient (DSC) as HiLab. Our code is available\u00a0at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/WltyBY\/HNTS-MRG2024_train_code\" ext-link-type=\"uri\">https:\/\/github.com\/WltyBY\/HNTS-MRG2024_train_code<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_5","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:30Z","timestamp":1740919350000},"page":"75-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Head and\u00a0Neck Tumor Segmentation of MRI from Pre- and Mid-Radiotherapy with Pre-Training, Data Augmentation and Dual Flow UNet"],"prefix":"10.1007","author":[{"given":"Litingyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-158X","authenticated-orcid":false,"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Andrearczyk, V., et\u00a0al.: 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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