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@INPROCEEDINGS{Maier:268788,
      author       = {Singh, Devesh and Dyrba, Martin},
      editor       = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz
                      and Maier-Hein, Klaus and Palm, Christoph and Tolxdorff,
                      Thomas},
      title        = {{C}omputational {O}ntology and {V}isualization {F}ramework
                      for the {V}isual {C}omparison of {B}rain {A}trophy
                      {P}rofiles},
      address      = {Wiesbaden},
      publisher    = {Springer Fachmedien Wiesbaden},
      reportid     = {DZNE-2024-00331},
      isbn         = {978-3-658-44036-7 (print)},
      series       = {Informatik aktuell},
      pages        = {149 - 154},
      year         = {2024},
      note         = {Missing Journal: = 2628-8958 (import from CrossRef Book
                      Series, Journals: pub.dzne.de)},
      comment      = {Bildverarbeitung für die Medizin 2024 / Maier, Andreas
                      (Editor) [https://orcid.org/0000-0002-9550-5284] ; Wiesbaden
                      : Springer Fachmedien Wiesbaden, 2024, Chapter 43 ; ISSN:
                      1431-472X=2628-8958 ; ISBN:
                      978-3-658-44036-7=978-3-658-44037-4 ;
                      doi:10.1007/978-3-658-44037-4},
      booktitle     = {Bildverarbeitung für die Medizin 2024
                       / Maier, Andreas (Editor)
                       [https://orcid.org/0000-0002-9550-5284]
                       ; Wiesbaden : Springer Fachmedien
                       Wiesbaden, 2024, Chapter 43 ; ISSN:
                       1431-472X=2628-8958 ; ISBN:
                       978-3-658-44036-7=978-3-658-44037-4 ;
                       doi:10.1007/978-3-658-44037-4},
      abstract     = {Alzheimer’s disease (AD) accounts for more than
                      two-thirds of all dementia cases. Existing MRI volumetry
                      tools summarize pathology found within brain MRI scans.
                      However, they often lack methods for aggregating information
                      at different brain abstraction levels, and lack an intuitive
                      visualizations.We propose a computational pipeline for
                      quantifying hierarchical volumetric deviations and
                      generating interactive summary visualizations. We collected
                      N=3115 MRI scans from five different data cohorts. We used
                      the FastSurferCNN tool to obtain brain region segmentations
                      and estimate their raw volumes. First, we created a semantic
                      model, encoding hierarchical anatomical relationships in the
                      web ontology language (OWL) model and a computational
                      framework for aggregating volumetric deviations. Second,we
                      developed a visualization framework, providing interactive
                      visual ‘sunburst’ summary plots. The summary plots can
                      highlight mean-group or single-subject atrophy profiles,
                      enhancing visual comparison of atrophy profiles with
                      different AD phases. Our pipeline could assist clinicians in
                      discovering brain pathologies or subgroups in an
                      interpretable and reliable manner.},
      month         = {Mar},
      date          = {2024-03-10},
      organization  = {German Conference on Medical Image
                       Computing, Erlangen (Germany), 10 Mar
                       2024 - 12 Mar 2024},
      cin          = {AG Teipel},
      cid          = {I:(DE-2719)1510100},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-658-44037-4_43},
      url          = {https://pub.dzne.de/record/268788},
}