Recent advances in deep learning based image segmentation
methods have enabled real-time performance with human-level accuracy.
However, occasionally even the best method fails due to low image qual-
ity, artifacts or unexpected behaviour of black box algorithms. Being
able to predict segmentation quality in the absence of ground truth is of
paramount importance in clinical practice, but also in large-scale studies
to avoid the inclusion of invalid data in subsequent analysis.
In this work, we propose two approaches of real-time automated quality
control for cardiovascular MR segmentations using deep learning. First,
we train a neural network on 12,880 samples to predict Dice Similarity
Coefficients (DSC) on a per-case basis. We report a mean average error
(MAE) of 0.03 on 1,610 test samples and 97% binary classification accu-
racy for separating low and high quality segmentations. Secondly, in the
scenario where no manually annotated data is available, we train a net-
work to predict DSC scores from estimated quality obtained via a reverse
testing strategy. We report an MAE = 0.14 and 91% binary classifica-
tion accuracy for this case. Predictions are obtained in real-time which,
when combined with real-time segmentation methods, enables instant
feedback on whether an acquired scan is analysable while the patient is
still in the scanner. This further enables new applications of optimising
image acquisition towards best possible analysis results.