  Segmentation of cardiac anatomical structures in cardiac magnetic resonance
images (CMRI) is a prerequisite for automatic diagnosis and prognosis of
cardiovascular diseases. To increase robustness and performance of segmentation
methods this study combines automatic segmentation and assessment of
segmentation uncertainty in CMRI to detect image regions containing local
segmentation failures. Three state-of-the-art convolutional neural networks
(CNN) were trained to automatically segment cardiac anatomical structures and
obtain two measures of predictive uncertainty: entropy and a measure derived by
MC-dropout. Thereafter, using the uncertainties another CNN was trained to
detect local segmentation failures that potentially need correction by an
expert. Finally, manual correction of the detected regions was simulated. Using
publicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact of
CNN architecture and loss function for segmentation, and the uncertainty
measure was investigated. Performance was evaluated using the Dice coefficient
and 3D Hausdorff distance between manual and automatic segmentation. The
experiments reveal that combining automatic segmentation with simulated manual
correction of detected segmentation failures leads to statistically significant
performance increase.
