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Fig. 4 | Journal of Cardiovascular Magnetic Resonance

Fig. 4

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

Fig. 4

Validation on 4805 Random Forest segmentations of UKBB Imaging Study with Ground Truth. 4,805 cardiac MRI were segmented with a Random Forest classifier. 500 trees and depths in the range [5 40] were used to simulate various degrees of segmentation quality. Manual contours were available through Biobank Application 2964. 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). All calculations on the whole-heart binary classification task. We report low mean absolute error (MAE) for all metrics and 95% binary classification accuracy (TPR = 0.97 and FPR = 0.15) with a DSC threshold of 0.70. High accuracy for individual segmentation classes

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