  When introducing advanced image computing algorithms, e.g., whole-heart
segmentation, into clinical practice, a common suspicion is how reliable the
automatically computed results are. In fact, it is important to find out the
failure cases and identify the misclassified pixels so that they can be
excluded or corrected for the subsequent analysis or diagnosis. However, it is
not a trivial problem to predict the errors in a segmentation mask when ground
truth (usually annotated by experts) is absent. In this work, we attempt to
address the pixel-wise error map prediction problem and the per-case mask
quality assessment problem using a unified deep learning (DL) framework.
Specifically, we first formalize an error map prediction problem, then we
convert it to a segmentation problem and build a DL network to tackle it. We
also derive a quality indicator (QI) from a predicted error map to measure the
overall quality of a segmentation mask. To evaluate the proposed framework, we
perform extensive experiments on a public whole-heart segmentation dataset,
i.e., MICCAI 2017 MMWHS. By 5-fold cross validation, we obtain an overall Dice
score of 0.626 for the error map prediction task, and observe a high Pearson
correlation coefficient (PCC) of 0.972 between QI and the actual segmentation
accuracy (Acc), as well as a low mean absolute error (MAE) of 0.0048 between
them, which evidences the efficacy of our method in both error map prediction
and quality assessment.
