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@ARTICLE{Dnnwald:278656,
      author       = {Dünnwald, Max and Krohn, Friedrich and Sciarra, Alessandro
                      and Sarkar, Mousumi and Schneider, Anja and Fliessbach,
                      Klaus and Kimmich, Okka and Jessen, Frank and Rostamzadeh,
                      Ayda and Glanz, Wenzel and Incesoy, Enise I and Teipel,
                      Stefan and Kilimann, Ingo and Goerss, Doreen and Spottke,
                      Annika and Brustkern, Johanna and Heneka, Michael T and
                      Brosseron, Frederic and Lüsebrink, Falk and Hämmerer,
                      Dorothea and Düzel, Emrah and Tönnies, Klaus and
                      Oeltze-Jafra, Steffen and Betts, Matthew J},
      title        = {{F}ully automated {MRI}-based analysis of the locus
                      coeruleus in aging and {A}lzheimer's disease dementia using
                      {ELSI}-{N}et.},
      journal      = {Alzheimer's $\&$ dementia / Diagnosis, assessment $\&$
                      disease monitoring},
      volume       = {17},
      number       = {2},
      issn         = {2352-8729},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DZNE-2025-00612},
      pages        = {e70118},
      year         = {2025},
      abstract     = {The locus coeruleus (LC) is linked to the development and
                      pathophysiology of neurodegenerative diseases such as
                      Alzheimer's disease (AD). Magnetic resonance imaging-based
                      LC features have shown potential to assess LC integrity in
                      vivo.We present a deep learning-based LC segmentation and
                      feature extraction method called Ensemble-based Locus
                      Coeruleus Segmentation Network (ELSI-Net) and apply it to
                      healthy aging and AD dementia datasets. Agreement to expert
                      raters and previously published LC atlases were assessed. We
                      aimed to reproduce previously reported differences in LC
                      integrity in aging and AD dementia and correlate extracted
                      features to cerebrospinal fluid (CSF) biomarkers of AD
                      pathology.ELSI-Net demonstrated high agreement to expert
                      raters and published atlases. Previously reported group
                      differences in LC integrity were detected and correlations
                      to CSF biomarkers were found.Although we found excellent
                      performance, further evaluations on more diverse datasets
                      from clinical cohorts are required for a conclusive
                      assessment of ELSI-Net's general applicability.We provide a
                      thorough evaluation of a fully automatic locus coeruleus
                      (LC) segmentation method termed Ensemble-based Locus
                      Coeruleus Segmentation Network (ELSI-Net) in aging and
                      Alzheimer's disease (AD) dementia.ELSI-Net outperforms
                      previous work and shows high agreement with manual ratings
                      and previously published LC atlases.ELSI-Net replicates
                      previously shown LC group differences in aging and
                      AD.ELSI-Net's LC mask volume correlates with cerebrospinal
                      fluid biomarkers of AD pathology.},
      keywords     = {biomarker (Other) / deep learning (Other) / locus coeruleus
                      (Other) / magnetic resonance imaging (Other) / segmentation
                      (Other)},
      cin          = {AG Düzel / AG Schneider / Patient Studies (Bonn) /
                      Clinical Research (Bonn) / AG Spottke / AG Jessen / AG
                      Teipel / AG Heneka},
      ddc          = {610},
      cid          = {I:(DE-2719)5000006 / I:(DE-2719)1011305 /
                      I:(DE-2719)1011101 / I:(DE-2719)1011001 / I:(DE-2719)1011103
                      / I:(DE-2719)1011102 / I:(DE-2719)1510100 /
                      I:(DE-2719)1011303},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40365469},
      pmc          = {pmc:PMC12069022},
      doi          = {10.1002/dad2.70118},
      url          = {https://pub.dzne.de/record/278656},
}