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| Home > Publications database > Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy. > print |
| 001 | 300814 | ||
| 005 | 20250508113029.0 | ||
| 020 | _ | _ | |a 978-3-031-83273-4 (print) |
| 020 | _ | _ | |a 978-3-031-83274-1 (electronic) |
| 024 | 7 | _ | |a 10.1007/978-3-031-83274-1_3 |2 doi |
| 024 | 7 | _ | |a pmid:40291013 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC12023904 |2 pmc |
| 024 | 7 | _ | |a 0302-9743 |2 ISSN |
| 024 | 7 | _ | |a 1611-3349 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2025-00928 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Kächele, Jessica |0 P:(DE-He78)05779b8fc2a612fdf8364db690f3480c |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy. |
| 260 | _ | _ | |a Cham |c 2025 |b Springer Nature Switzerland |
| 295 | 1 | 0 | |a Head and Neck Tumor Segmentation for MR-Guided Applications / Wahid, Kareem A. (Editor) [https://orcid.org/0000-0002-0503-0175] ; Cham : Springer Nature Switzerland, 2025, Chapter 3 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-031-83273-4=978-3-031-83274-1 ; doi:10.1007/978-3-031-83274-1 |
| 300 | _ | _ | |a 50 - 64 |
| 336 | 7 | _ | |a BOOK_CHAPTER |2 ORCID |
| 336 | 7 | _ | |a Book Section |0 7 |2 EndNote |
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| 336 | 7 | _ | |a INBOOK |2 BibTeX |
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| 336 | 7 | _ | |a Contribution to a book |b contb |m contb |0 PUB:(DE-HGF)7 |s 1746695886_30 |2 PUB:(DE-HGF) |
| 490 | 0 | _ | |a Lecture Notes in Computer Science |v 15273 |
| 520 | _ | _ | |a The increasing utilization of MRI in radiation therapy planning for head and neck cancer (HNC) highlights the need for precise tumor segmentation to enhance treatment efficacy and reduce side effects. This work presents segmentation models developed for the HNTS-MRG 2024 challenge by the team mic-dkfz, focusing on automated segmentation of HNC tumors from MRI images at two radiotherapy (RT) stages: before (pre-RT) and 2-4 weeks into RT (mid-RT). For Task 1 (pre-RT segmentation), we built upon the nnU-Net framework, enhancing it with the larger Residual Encoder architecture. We incorporated extensive data augmentation and applied transfer learning by pre-training the model on a diverse set 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 tumor (GTVp) segmentation is challenging and presents potential for further optimization. These results demonstrate the effectiveness of combining advanced architectures, transfer learning, and longitudinal data integration for automated tumor segmentation in MRI-guided adaptive radiation therapy. |
| 536 | _ | _ | |a 315 - Bildgebung und Radioonkologie (POF4-315) |0 G:(DE-HGF)POF4-315 |c POF4-315 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef Book Series, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a Head and Neck Cancer |2 Other |
| 650 | _ | 7 | |a Longitudinal Data Integration |2 Other |
| 650 | _ | 7 | |a MRI-guided Radiation Therapy |2 Other |
| 650 | _ | 7 | |a Transfer Learning |2 Other |
| 650 | _ | 7 | |a Tumor Segmentation |2 Other |
| 650 | _ | 7 | |a nnU-Net |2 Other |
| 700 | 1 | _ | |a Zenk, Maximilian |0 P:(DE-He78)eafef5cb69dd3d85f1cc942c474a220f |b 1 |u dkfz |
| 700 | 1 | _ | |a Rokuss, Maximilian |0 P:(DE-He78)936ebccdc011e3efd9ffc0bdcc2d8379 |b 2 |u dkfz |
| 700 | 1 | _ | |a Ulrich, Constantin |0 P:(DE-He78)1bf529d39d90e30ceb901da6e5816185 |b 3 |u dkfz |
| 700 | 1 | _ | |a Wald, Tassilo |0 P:(DE-He78)4412d586f86ca57943732a2b9318c44f |b 4 |u dkfz |
| 700 | 1 | _ | |a Maier-Hein, Klaus |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 5 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1007/978-3-031-83274-1_3 |
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