  In recent years, convolutional neural networks have demonstrated promising
performance in a variety of medical image segmentation tasks. However, when a
trained segmentation model is deployed into the real clinical world, the model
may not perform optimally. A major challenge is the potential poor-quality
segmentations generated due to degraded image quality or domain shift issues.
There is a timely need to develop an automated quality control method that can
detect poor segmentations and feedback to clinicians. Here we propose a novel
deep generative model-based framework for quality control of cardiac MRI
segmentation. It first learns a manifold of good-quality image-segmentation
pairs using a generative model. The quality of a given test segmentation is
then assessed by evaluating the difference from its projection onto the
good-quality manifold. In particular, the projection is refined through
iterative search in the latent space. The proposed method achieves high
prediction accuracy on two publicly available cardiac MRI datasets. Moreover,
it shows better generalisation ability than traditional regression-based
methods. Our approach provides a real-time and model-agnostic quality control
for cardiac MRI segmentation, which has the potential to be integrated into
clinical image analysis workflows.
