% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Rathore:1049628,
      author       = {Pfaehler, Elisabeth},
      collaboration = {Paul, Richard Dominik},
      othercontributors = {Rathore, Omini and Morrison, Abigail and Scharr, Hanno},
      title        = {{E}fficient {E}pistemic {U}ncertainty {E}stimation in
                      {C}erebrovascular {S}egmentation},
      reportid     = {FZJ-2025-05416, arXiv:2503.22271},
      year         = {2025},
      abstract     = {Brain vessel segmentation of MR scans is a critical step in
                      the diagnosis of cerebrovascular diseases. Due to the fine
                      vessel structure, manual vessel segmentation is time
                      consuming. Therefore, automatic deep learning (DL) based
                      segmentation techniques are intensively investigated. As
                      conventional DL models yield a high complexity and lack an
                      indication of decision reliability, they are often
                      considered as not trustworthy. This work aims to increase
                      trust in DL based models by incorporating epistemic
                      uncertainty quantification into cerebrovascular segmentation
                      models for the first time. By implementing an efficient
                      ensemble model combining the advantages of Bayesian
                      Approximation and Deep Ensembles, we aim to overcome the
                      high computational costs of conventional probabilistic
                      networks. Areas of high model uncertainty and erroneous
                      predictions are aligned which demonstrates the effectiveness
                      and reliability of the approach. We perform extensive
                      experiments applying the ensemble model on
                      out-of-distribution (OOD) data. We demonstrate that for
                      OOD-images, the estimated uncertainty increases.
                      Additionally, omitting highly uncertain areas improves the
                      segmentation quality, both for in- and out-of-distribution
                      data. The ensemble model explains its limitations in a
                      reliable manner and can maintain trustworthiness also for
                      OOD data and could be considered in clinical applications},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2503.22271},
      howpublished = {arXiv:2503.22271},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2503.22271;\%\%$},
      doi          = {10.34734/FZJ-2025-05416},
      url          = {https://juser.fz-juelich.de/record/1049628},
}