Deep ensemble learning-based quality control for automatic segmentation in cardiovascular magnetic resonance imaging

Thesis

Cardiovascular magnetic resonance (CMR) imaging is a powerful tool for research and clinical applications. To extract useful clinical information from the acquired CMR images, time-consuming and laborious manual delineation of cardiovascular structures is currently required. Despite promising overall performance across medical imaging applications, the current state-of-the-art automated image segmentation methods still fail in some cases, potentially jeopardising the reliability of clinica...

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Authors: Hann, E

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Quality control; Medical image analysis; Uncertainty estimation; Cardiovascular magnetic resonance; Deep learning; T1 mapping; UK Biobank; Image segmentation; Ensemble learning
• Subjects: Supervised learning (Machine learning); Magnetic resonance imaging; Machine learning; Ensemble learning (Machine learning); Diagnostic imaging; Image processing; Image segmentation; Artificial intelligence; Neural networks (Computer science)