{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T12:27:26Z","timestamp":1787747246976,"version":"build-2784847793"},"publisher-location":"Cham","reference-count":15,"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                    Tumor volume segmentation on MRI is a challenging and time-consuming process that is performed manually in typical clinical settings. This work presents an approach to automated delineation of head and neck tumors on MRI scans, developed in the context of the MICCAI Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge. Rather than designing a new, task-specific convolutional neural network, the focus of this research was to propose improvements to the configuration commonly used in medical segmentation tasks, relying solely on the traditional U-Net architecture. The empirical results presented in this article suggest the superiority of patch-wise normalization used for both training and sliding window inference. They also indicate that the performance of segmentation models can be enhanced by applying a scheduled data augmentation policy during training. Finally, it is shown that a small improvement in quality can be achieved by using Gaussian weighting to combine predictions for individual patches during sliding window inference. The model with the best configuration obtained an aggregated Dice Similarity Coefficient (DSCagg) of 0.749 in Task\u00a01 and 0.710 in Task\u00a02 on five cross-validation folds. The ensemble of five models (one best model per validation fold) showed consistent results on a private test set of 50 patients with an DSCagg of 0.752 in Task\u00a01 and 0.718 in Task\u00a02 (team name: ). The source code and model weights are freely available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.github.com\/iantsen\/hntsmrg\" ext-link-type=\"uri\">www.github.com\/iantsen\/hntsmrg<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_18","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T07:42:22Z","timestamp":1740901342000},"page":"230-240","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving the\u00a0U-Net Configuration for\u00a0Automated Delineation of\u00a0Head and\u00a0Neck Cancer on\u00a0MRI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6690-6070","authenticated-orcid":false,"given":"Andrei","family":"Iantsen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"issue":"3","key":"18_CR1","doi-asserted-by":"publisher","first-page":"406","DOI":"10.2967\/jnumed.116.180919","volume":"58","author":"MC Desseroit","year":"2016","unstructured":"Desseroit, M.C., et al.: Reliability of PET\/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. 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