Fig. 1.

Overview of the problem of predicting the quality of organ segmentations. First, a segmentation model (not covered in this article) takes images as input and produces segmentation maps. Then, our quality prediction model takes both the images and the segmentation maps as input and produces an estimate of the quality of the segmentation—in this case the Dice similarity coefficient. Good contours have a high Dice value and poor contours have a low Dice value. Note that we need ground truth segmentations in order to calculate the true Dice value and train the quality prediction model with supervised learning. In this study, we used 60 ground truth segmentations and 80 automatically generated masks along with heavy data augmentation to train the quality prediction model