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Table 2 Initial reverse classification accuracy validation on 400 random forest segmentations

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

Class Acc. TPR FPR MAE
     DSC MSD RMS HD
      mm mm mm
LVC 0.973 0.977 0.036 0.020 4.104 5.593 14.15
  0.980 0.975 0.019     
LVM 0.815 0.947 0.215 0.044 3.756 4.741 13.08
  0.990 0.987 0.008     
RVC 0.985 0.923 0.012 0.030 4.104 5.022 16.63
  0.943 0.914 0.047     
Av. 0.924 0.949 0.089 0.031 3.988 5.119 14.62
  0.971 0.959 0.025     
WH 0.988 0.979 0.000 0.029 4.445 5.504 15.11
  0.948 0.886 0.047     
  1. Classes are LV Cavity (LVC), LV Myocardium (LVM), RV Cavity (RVC), An average over the classes (Av.) and a binary segmentation of the whole heart (WH). First row for each class shows the binary classification accuracy for ‘poor’ and ‘good’ segmentations in the Dice Similarity Coefficient (DSC) ranges [0.0 0.7) and [0.7 1.0] respectively. Second row for each class shows the binary classification accuracy for ‘poor’ and ‘good’ segmentations in the Mean Surface Distance (MSD) ranges [>2.0mm] and [0.0mm 2.0mm] respectively. True-positive and false-positive rates are also shown. We report mean absolute errors (MAE) on the predictions of DSC and additional surface-distance metrics: root-mean-squared surface distance (RMS) and Hausdorff distance (HD)