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Fig. 3 | Journal of Cardiovascular Magnetic Resonance

Fig. 3

From: Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

Fig. 3

RCA Validation on 400 cardiac MRI. 400 cardiac MRI segmentations were generated with a Random Forest classifier. 500 trees and depths in the range [5, 40] were used to simulate various degrees of segmentation quality. RCA with single-atlas classifier was used to predict the Dice Similarity Coefficient (DSC), mean surface distance (MSD), root mean-squared surface distance (RMS) and Hausdorff distance (HD). Ground truth for the scans is known so real metrics are also calculated. All calculations on the whole-heart binary classification task. We report low mean absolute error (MAE) for all metrics and 99% binary classification accuracy (TPR = 0.98, FPR = 0.00) with a DSC threshold of 0.70. High accuracy for individual segmentation classes. Absolute error for each image is shown for each metric. We note increasing error with decreasing quality of segmentation based on the real metric score

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