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From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review
2022
Applied Sciences
Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR) image segmentation has achieved state-of-the-art performance. Despite achieving inter-observer variability in terms of different accuracy performance measures, visual inspections reveal errors in most segmentation results, indicating a lack of reliability and robustness of DL segmentation models, which can be critical if a model was to be deployed into clinical practice. In this work, we aim to bring
doi:10.3390/app12083936
fatcat:p4rg6p27dbgr5ju44wireqwjaa