001038863 001__ 1038863
001038863 005__ 20250205221805.0
001038863 037__ $$aFZJ-2025-01679
001038863 041__ $$aEnglish
001038863 1001_ $$0P:(DE-Juel1)200339$$aRathore, Omini$$b0$$ufzj
001038863 245__ $$aUncertainty Estimation in the Segmentation of Brain Magnetic Resonance Angiograms$$f - 2024-12-27
001038863 260__ $$c2025
001038863 300__ $$a52
001038863 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
001038863 3367_ $$02$$2EndNote$$aThesis
001038863 3367_ $$2BibTeX$$aMASTERSTHESIS
001038863 3367_ $$2DRIVER$$amasterThesis
001038863 3367_ $$0PUB:(DE-HGF)19$$2PUB:(DE-HGF)$$aMaster Thesis$$bmaster$$mmaster$$s1738752068_31120
001038863 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
001038863 502__ $$aMasterarbeit, RWTH, 2024$$bMasterarbeit$$cRWTH$$d2024$$o2024-12-27
001038863 520__ $$aAbstract:The analysis of brain vessel scans is a critical step in detecting cerebrovascular diseases.Manual segmentation of cerebrovascular structures is a labour-intensive process susceptibleto inter-observer variability, which has led to extensive research into automatic segmentationusing deep learning techniques. These methods have yielded promising models;however, their opaque nature and methodological complexity hinder understanding, interpretability,and adoption by clinicians in practical settings. Furthermore, MagneticResonance Angiogram data is inherently complex, as cerebrovascular structures exhibitsignificant variability across individuals and scanners, further challenging the reliability ofautomated models.This thesis seeks to bridge this gap by incorporating uncertainty quantification techniquesinto deep learning-based cerebrovascular segmentation models. Uncertainty quantificationmeasures the model’s confidence in its predictions, providing insights that enhance interpretability.Moreover, uncertainty maps can identify regions with elevated uncertainty,thereby increasing clinicians’ trust in the model. In this work, we implement an efficientensemble model utilizing Bayesian Approximation to estimate uncertainty. The alignmentbetween areas of high uncertainty and erroneous predictions demonstrates the effectivenessof the ensemble model in generating reliable uncertainty maps. The proposed model outperformsthe baseline point-estimate model, with further improvements achieved throughthe integration of uncertainty estimates.The model’s robustness is further validated on out-of-distribution datasets, including MRAscans from diverse scanners and populations of both healthy and unhealthy patients. Theresulting uncertainty maps elucidate the higher error rates in these datasets, reinforcingthe model’s ability to explain its limitations and maintain trustworthiness.iii
001038863 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001038863 909CO $$ooai:juser.fz-juelich.de:1038863$$pVDB
001038863 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)200339$$aForschungszentrum Jülich$$b0$$kFZJ
001038863 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001038863 9141_ $$y2025
001038863 920__ $$lyes
001038863 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x0
001038863 980__ $$amaster
001038863 980__ $$aVDB
001038863 980__ $$aI:(DE-Juel1)IAS-8-20210421
001038863 980__ $$aUNRESTRICTED