  Deep learning methods have reached state-of-the-art performance in cardiac
image segmentation. Currently, the main bottleneck towards their effective
translation into clinics requires assuring continuous high model performance
and segmentation results. In this work, we present a novel learning framework
to monitor the performance of heart segmentation models in the absence of
ground truth. Formulated as an anomaly detection problem, the monitoring
framework allows deriving surrogate quality measures for a segmentation and
allows flagging suspicious results. We propose two different types of quality
measures, a global score and a pixel-wise map. We demonstrate their use by
reproducing the final rankings of a cardiac segmentation challenge in the
absence of ground truth. Results show that our framework is accurate, fast, and
scalable, confirming it is a viable option for quality control monitoring in
clinical practice and large population studies.
