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This work presents segmentation models developed\u00a0for the HNTS-MRG 2024 challenge by the team mic-dkfz, focusing\u00a0on automated segmentation of HNC tumors from MRI images at\u00a0two radiotherapy (RT) stages: before (pre-RT) and 2\u20134 weeks into\u00a0RT (mid-RT). For Task 1 (pre-RT segmentation), we built upon\u00a0the nnU-Net framework, enhancing it with the larger Residual Encoder architecture. We incorporated extensive data augmentation\u00a0and applied transfer learning by pretraining the model on a diverse\u00a0set of public 3D medical imaging datasets. For Task 2 (mid-RT segmentation), we adopted a longitudinal approach by integrating registered pre-RT images and their segmentations as additional inputs into the nnU-Net framework. On the test set, our models achieved mean aggregated Dice Similarity Coefficient (aggDSC) scores of 81.2 for Task 1 and 72.7 for Task 2. Especially the primary\u00a0tumor (GTVp) segmentation is challenging and presents potential\u00a0for further optimization. These results demonstrate the effectiveness\u00a0of combining advanced architectures, transfer learning,\u00a0and longitudinal data integration for automated tumor segmentation\u00a0in MRI-guided adaptive radiation therapy.<\/jats:p>","DOI":"10.1007\/978-3-031-83274-1_3","type":"book-chapter","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T12:42:24Z","timestamp":1740919344000},"page":"50-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhanced nnU-Net Architectures for\u00a0Automated MRI Segmentation of\u00a0Head and\u00a0Neck Tumors in\u00a0Adaptive Radiation Therapy"],"prefix":"10.1007","author":[{"given":"Jessica","family":"K\u00e4chele","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maximilian","family":"Zenk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maximilian","family":"Rokuss","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Constantin","family":"Ulrich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tassilo","family":"Wald","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus H.","family":"Maier-Hein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"issue":"1073","key":"3_CR1","doi-asserted-by":"publisher","first-page":"20160667","DOI":"10.1259\/bjr.20160667","volume":"90","author":"JM Pollard","year":"2017","unstructured":"Pollard, J.M., Wen, Z., Sadagopan, R., Wang, J., Ibbott, G.S.: The future of image-guided radiotherapy will be MR guided. 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