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@ARTICLE{Chatterjee:278930,
      author       = {Chatterjee, Soumick and Gaidzik, Franziska and Sciarra,
                      Alessandro and Mattern, Hendrik and Janiga, Gábor and
                      Speck, Oliver and Nürnberger, Andreas and Pathiraja,
                      Sahani},
      title        = {{PULAS}ki: {L}earning inter-rater variability using
                      statistical distances to improve probabilistic
                      segmentation.},
      journal      = {Medical image analysis},
      volume       = {103},
      issn         = {1361-8415},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2025-00656},
      pages        = {103623},
      year         = {2025},
      abstract     = {In the domain of medical imaging, many supervised learning
                      based methods for segmentation face several challenges such
                      as high variability in annotations from multiple experts,
                      paucity of labelled data and class imbalanced datasets.
                      These issues may result in segmentations that lack the
                      requisite precision for clinical analysis and can be
                      misleadingly overconfident without associated uncertainty
                      quantification. This work proposes the PULASki method as a
                      computationally efficient generative tool for biomedical
                      image segmentation that accurately captures variability in
                      expert annotations, even in small datasets. This approach
                      makes use of an improved loss function based on statistical
                      distances in a conditional variational autoencoder structure
                      (Probabilistic UNet) , which improves learning of the
                      conditional decoder compared to the standard cross-entropy
                      particularly in class imbalanced problems. The proposed
                      method was analysed for two structurally different
                      segmentation tasks (intracranial vessel and multiple
                      sclerosis (MS) lesion) and compare our results to four
                      well-established baselines in terms of quantitative metrics
                      and qualitative output. These experiments involve
                      class-imbalanced datasets characterised by challenging
                      features, including suboptimal signal-to-noise ratios and
                      high ambiguity. Empirical results demonstrate the PULASKi
                      method outperforms all baselines at the $5\%$ significance
                      level. Our experiments are also of the first to present a
                      comparative study of the computationally feasible
                      segmentation of complex geometries using 3D patches and the
                      traditional use of 2D slices. The generated segmentations
                      are shown to be much more anatomically plausible than in the
                      2D case, particularly for the vessel task. Our method can
                      also be applied to a wide range of multi-label segmentation
                      tasks and is useful for downstream tasks such as hemodynamic
                      modelling (computational fluid dynamics and data
                      assimilation), clinical decision making, and treatment
                      planning.},
      keywords     = {Humans / Observer Variation / Magnetic Resonance Imaging:
                      methods / Algorithms / Image Interpretation,
                      Computer-Assisted: methods / Multiple Sclerosis: diagnostic
                      imaging / Supervised Machine Learning / Conditional VAE
                      (Other) / Distribution distance (Other) / Multiple sclerosis
                      segmentation (Other) / Probabilistic UNet (Other) / Vessel
                      segmentation (Other)},
      cin          = {AG Schreiber / AG Speck},
      ddc          = {610},
      cid          = {I:(DE-2719)1310010 / I:(DE-2719)1340009},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40367700},
      doi          = {10.1016/j.media.2025.103623},
      url          = {https://pub.dzne.de/record/278930},
}