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@ARTICLE{Langner:301263,
      author       = {D. Langner and M. Nachbar and M. L. Russo and S. Boeke and
                      C. Gani and M. Niyazi and D. Thorwarth$^*$},
      title        = {{C}omparative analysis of open-source against commercial
                      {AI}-based segmentation models for online adaptive
                      {MR}-guided radiotherapy.},
      journal      = {Zeitschrift für medizinische Physik},
      volume       = {nn},
      issn         = {0939-3889},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2025-00948},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {Online adaptive magnetic resonance-guided radiotherapy
                      (MRgRT) has emerged as a state-of-the-art treatment option
                      for multiple tumour entities, accounting for daily
                      anatomical and tumour volume changes, thus allowing sparing
                      of relevant organs at risk (OARs). However, the annotation
                      of treatment-relevant anatomical structures in context of
                      online plan adaptation remains challenging, often relying on
                      commercial segmentation solutions due to limited
                      availability of clinically validated alternatives. The aim
                      of this study was to investigate whether an open-source
                      artificial intelligence (AI) segmentation network can
                      compete with the annotation accuracy of a commercial
                      solution, both trained on the identical dataset, questioning
                      the need for commercial models in clinical practice.For 47
                      pelvic patients, T2w MR imaging data acquired on a 1.5 T
                      MR-Linac were manually contoured, identifying prostate,
                      seminal vesicles, rectum, anal canal, bladder, penile bulb,
                      and bony structures. These training data were used for the
                      generation of an in-house AI segmentation model, a nnU-Net
                      with residual encoder architecture featuring a streamlined
                      single image inference pipeline, and re-training of a
                      commercial solution. For quantitative evaluation, 20 MR
                      images were contoured by a radiation oncologist, considered
                      as ground truth contours (GTC) and compared with the
                      in-house/commercial AI-based contours (iAIC/cAIC) using Dice
                      Similarity Coefficient (DSC), $95\%$ Hausdorff distances
                      (HD95), and surface DSC (sDSC). For qualitative evaluation,
                      four radiation oncologists assessed the usability of
                      OAR/target iAIC within an online adaptive workflow using a
                      four-point Likert scale: (1) acceptable without
                      modification, (2) requiring minor adjustments, (3) requiring
                      major adjustments, and (4) not usable.Patient-individual
                      annotations were generated in a median [range] time of 23
                      [16-34] s for iAIC and 152 [121-198] s for cAIC,
                      respectively. OARs showed a maximum median DSC of 0.97/0.97
                      (iAIC/cAIC) for bladder and minimum median DSC of 0.78/0.79
                      (iAIC/cAIC) for anal canal/penile bulb. Maximal respectively
                      minimal median HD95 were detected for rectum with 17.3/20.6
                      mm (iAIC/cAIC) and for bladder with 5.6/6.0 mm (iAIC/cAIC).
                      Overall, the average median DSC/HD95 values were 0.87/11.8mm
                      (iAIC) and 0.83/10.2mm (cAIC) for OAR/targets and
                      0.90/11.9mm (iAIC) and 0.91/16.5mm (cAIC) for bony
                      structures. For a tolerance of 3 mm, the highest and lowest
                      sDSC were determined for bladder (iAIC:1.00, cAIC:0.99) and
                      prostate in iAIC (0.89) and anal canal in cAIC (0.80),
                      respectively. Qualitatively, $84.8\%$ of analysed contours
                      were considered as clinically acceptable for iAIC, while
                      $12.9\%$ required minor and $2.3\%$ major adjustments or
                      were classed as unusable. Contour-specific analysis showed
                      that iAIC achieved the highest mean scores with 1.00 for the
                      anal canal and the lowest with 1.61 for the prostate.This
                      study demonstrates that open-source segmentation framework
                      can achieve comparable annotation accuracy to commercial
                      solutions for pelvic anatomy in online adaptive MRgRT. The
                      adapted framework not only maintained high segmentation
                      performance, with $84.8\%$ of contours accepted by
                      physicians or requiring only minor corrections $(12.9\%)$
                      but also enhanced clinical workflow efficiency of online
                      adaptive MRgRT through reduced inference times. These
                      findings establish open-source frameworks as viable
                      alternatives to commercial systems in supervised clinical
                      workflows.},
      keywords     = {Automatic annotation (Other) / Deep Learning (Other) /
                      MR-Linac (Other) / medical image segmentation (Other) /
                      online adaptive radiotherapy (Other)},
      cin          = {TU01},
      ddc          = {610},
      cid          = {I:(DE-He78)TU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40345918},
      doi          = {10.1016/j.zemedi.2025.04.008},
      url          = {https://inrepo02.dkfz.de/record/301263},
}