000300814 001__ 300814
000300814 005__ 20250508113029.0
000300814 020__ $$a978-3-031-83273-4 (print)
000300814 020__ $$a978-3-031-83274-1 (electronic)
000300814 0247_ $$2doi$$a10.1007/978-3-031-83274-1_3
000300814 0247_ $$2pmid$$apmid:40291013
000300814 0247_ $$2pmc$$apmc:PMC12023904
000300814 0247_ $$2ISSN$$a0302-9743
000300814 0247_ $$2ISSN$$a1611-3349
000300814 037__ $$aDKFZ-2025-00928
000300814 041__ $$aEnglish
000300814 1001_ $$0P:(DE-He78)05779b8fc2a612fdf8364db690f3480c$$aKächele, Jessica$$b0$$eFirst author$$udkfz
000300814 245__ $$aEnhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy.
000300814 260__ $$aCham$$bSpringer Nature Switzerland$$c2025
000300814 29510 $$aHead 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
000300814 300__ $$a50 - 64
000300814 3367_ $$2ORCID$$aBOOK_CHAPTER
000300814 3367_ $$07$$2EndNote$$aBook Section
000300814 3367_ $$2DRIVER$$abookPart
000300814 3367_ $$2BibTeX$$aINBOOK
000300814 3367_ $$2DataCite$$aOutput Types/Book chapter
000300814 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$bcontb$$mcontb$$s1746695886_30
000300814 4900_ $$aLecture Notes in Computer Science$$v15273
000300814 520__ $$aThe increasing utilization of MRI in radiation therapy planning for head and neck cancer (HNC) highlights the need for precise tumor segmentation to enhance treatment efficacy and reduce side effects. This work presents segmentation models developed for the HNTS-MRG 2024 challenge by the team mic-dkfz, focusing on automated segmentation of HNC tumors from MRI images at two radiotherapy (RT) stages: before (pre-RT) and 2-4 weeks into RT (mid-RT). For Task 1 (pre-RT segmentation), we built upon the nnU-Net framework, enhancing it with the larger Residual Encoder architecture. We incorporated extensive data augmentation and applied transfer learning by pre-training the model on a diverse set of public 3D medical imaging datasets. For Task 2 (mid-RT segmentation), we adopted a longitudinal approach by integrating registered pre-RT images and their segmentations as additional inputs into the nnU-Net framework. On the test set, our models achieved mean aggregated Dice Similarity Coefficient (aggDSC) scores of 81.2 for Task 1 and 72.7 for Task 2. Especially the primary tumor (GTVp) segmentation is challenging and presents potential for further optimization. These results demonstrate the effectiveness of combining advanced architectures, transfer learning, and longitudinal data integration for automated tumor segmentation in MRI-guided adaptive radiation therapy.
000300814 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000300814 588__ $$aDataset connected to CrossRef Book Series, PubMed, , Journals: inrepo02.dkfz.de
000300814 650_7 $$2Other$$aHead and Neck Cancer
000300814 650_7 $$2Other$$aLongitudinal Data Integration
000300814 650_7 $$2Other$$aMRI-guided Radiation Therapy
000300814 650_7 $$2Other$$aTransfer Learning
000300814 650_7 $$2Other$$aTumor Segmentation
000300814 650_7 $$2Other$$annU-Net
000300814 7001_ $$0P:(DE-He78)eafef5cb69dd3d85f1cc942c474a220f$$aZenk, Maximilian$$b1$$udkfz
000300814 7001_ $$0P:(DE-He78)936ebccdc011e3efd9ffc0bdcc2d8379$$aRokuss, Maximilian$$b2$$udkfz
000300814 7001_ $$0P:(DE-He78)1bf529d39d90e30ceb901da6e5816185$$aUlrich, Constantin$$b3$$udkfz
000300814 7001_ $$0P:(DE-He78)4412d586f86ca57943732a2b9318c44f$$aWald, Tassilo$$b4$$udkfz
000300814 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b5$$eLast author$$udkfz
000300814 773__ $$a10.1007/978-3-031-83274-1_3
000300814 909CO $$ooai:inrepo02.dkfz.de:300814$$pVDB
000300814 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)05779b8fc2a612fdf8364db690f3480c$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000300814 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)eafef5cb69dd3d85f1cc942c474a220f$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000300814 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)936ebccdc011e3efd9ffc0bdcc2d8379$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000300814 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)1bf529d39d90e30ceb901da6e5816185$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000300814 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)4412d586f86ca57943732a2b9318c44f$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000300814 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000300814 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000300814 9141_ $$y2025
000300814 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-28$$wger
000300814 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-28
000300814 9201_ $$0I:(DE-He78)E230-20160331$$kE230$$lE230 Medizinische Bildverarbeitung$$x0
000300814 9201_ $$0I:(DE-He78)HD01-20160331$$kHD01$$lDKTK HD zentral$$x1
000300814 980__ $$acontb
000300814 980__ $$aVDB
000300814 980__ $$aI:(DE-He78)E230-20160331
000300814 980__ $$aI:(DE-He78)HD01-20160331
000300814 980__ $$aUNRESTRICTED