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Fig. 5 | Journal of Cardiovascular Magnetic Resonance

Fig. 5

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

Fig. 5

Extensive Reverse Classification Accuracy Validation on 900 UKBB Segmentations. Convolutional neural network (CNN) segmentation as in Bai et al. [4]. 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 for the binary quality classification task on (top) ’Whole Heart’ average and (bottom) Left Ventricular Myocardium. We report low mean absolute error (MAE) for all metrics and 99.8% binary classification accuracy (TPR = 1.00 and FPR = 0.00) with a DSC threshold of 0.70

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