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@INBOOK{Kchele:300814,
      author       = {J. Kächele$^*$ and M. Zenk$^*$ and M. Rokuss$^*$ and C.
                      Ulrich$^*$ and T. Wald$^*$ and K. Maier-Hein$^*$},
      title        = {{E}nhanced nn{U}-{N}et {A}rchitectures for {A}utomated
                      {MRI} {S}egmentation of {H}ead and {N}eck {T}umors in
                      {A}daptive {R}adiation {T}herapy.},
      volume       = {15273},
      address      = {Cham},
      publisher    = {Springer Nature Switzerland},
      reportid     = {DKFZ-2025-00928},
      isbn         = {978-3-031-83273-4 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {50 - 64},
      year         = {2025},
      comment      = {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},
      booktitle     = {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},
      abstract     = {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.},
      keywords     = {Head and Neck Cancer (Other) / Longitudinal Data
                      Integration (Other) / MRI-guided Radiation Therapy (Other) /
                      Transfer Learning (Other) / Tumor Segmentation (Other) /
                      nnU-Net (Other)},
      cin          = {E230 / HD01},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)7},
      pubmed       = {pmid:40291013},
      pmc          = {pmc:PMC12023904},
      doi          = {10.1007/978-3-031-83274-1_3},
      url          = {https://inrepo02.dkfz.de/record/300814},
}