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| 001 | 1038863 | ||
| 005 | 20250205221805.0 | ||
| 037 | _ | _ | |a FZJ-2025-01679 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Rathore, Omini |0 P:(DE-Juel1)200339 |b 0 |u fzj |
| 245 | _ | _ | |a Uncertainty Estimation in the Segmentation of Brain Magnetic Resonance Angiograms |f - 2024-12-27 |
| 260 | _ | _ | |c 2025 |
| 300 | _ | _ | |a 52 |
| 336 | 7 | _ | |a Output Types/Supervised Student Publication |2 DataCite |
| 336 | 7 | _ | |a Thesis |0 2 |2 EndNote |
| 336 | 7 | _ | |a MASTERSTHESIS |2 BibTeX |
| 336 | 7 | _ | |a masterThesis |2 DRIVER |
| 336 | 7 | _ | |a Master Thesis |b master |m master |0 PUB:(DE-HGF)19 |s 1738752068_31120 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a SUPERVISED_STUDENT_PUBLICATION |2 ORCID |
| 502 | _ | _ | |a Masterarbeit, RWTH, 2024 |c RWTH |b Masterarbeit |d 2024 |o 2024-12-27 |
| 520 | _ | _ | |a 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 |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1038863 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)200339 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2025 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 0 |
| 980 | _ | _ | |a master |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-8-20210421 |
| 980 | _ | _ | |a UNRESTRICTED |
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