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@MASTERSTHESIS{Rathore:1038863,
      author       = {Rathore, Omini},
      title        = {{U}ncertainty {E}stimation in the {S}egmentation of {B}rain
                      {M}agnetic {R}esonance {A}ngiograms},
      school       = {RWTH},
      type         = {Masterarbeit},
      reportid     = {FZJ-2025-01679},
      pages        = {52},
      year         = {2025},
      note         = {Masterarbeit, RWTH, 2024},
      abstract     = {Abstract:The analysis of brain vessel scans is a critical
                      step in detecting cerebrovascular diseases.Manual
                      segmentation of cerebrovascular structures is a
                      labour-intensive process susceptibleto inter-observer
                      variability, which has led to extensive research into
                      automatic segmentationusing deep learning techniques. These
                      methods have yielded promising models;however, their opaque
                      nature and methodological complexity hinder understanding,
                      interpretability,and adoption by clinicians in practical
                      settings. Furthermore, MagneticResonance Angiogram data is
                      inherently complex, as cerebrovascular structures
                      exhibitsignificant variability across individuals and
                      scanners, further challenging the reliability ofautomated
                      models.This thesis seeks to bridge this gap by incorporating
                      uncertainty quantification techniquesinto deep
                      learning-based cerebrovascular segmentation models.
                      Uncertainty quantificationmeasures the model’s confidence
                      in its predictions, providing insights that enhance
                      interpretability.Moreover, uncertainty maps can identify
                      regions with elevated uncertainty,thereby increasing
                      clinicians’ trust in the model. In this work, we implement
                      an efficientensemble model utilizing Bayesian Approximation
                      to estimate uncertainty. The alignmentbetween areas of high
                      uncertainty and erroneous predictions demonstrates the
                      effectivenessof the ensemble model in generating reliable
                      uncertainty maps. The proposed model outperformsthe baseline
                      point-estimate model, with further improvements achieved
                      throughthe integration of uncertainty estimates.The
                      model’s robustness is further validated on
                      out-of-distribution datasets, including MRAscans from
                      diverse scanners and populations of both healthy and
                      unhealthy patients. Theresulting uncertainty maps elucidate
                      the higher error rates in these datasets, reinforcingthe
                      model’s ability to explain its limitations and maintain
                      trustworthiness.iii},
      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)19},
      url          = {https://juser.fz-juelich.de/record/1038863},
}