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Segmentation of cardiac anatomical structures in cardiac magnetic resonanceimages (CMRI) is a prerequisite for automatic diagnosis and prognosis ofcardiovascular diseases. To increase robustness and performance of segmentationmethods this study combines automatic segmentation and assessment ofsegmentation uncertainty in CMRI to detect image regions containing localsegmentation failures. Three state-of-the-art convolutional neural networks(CNN) were trained to automatically segment cardiac anatomical structures andobtain two measures of predictive uncertainty: entropy and a measure derived byMC-dropout. Thereafter, using the uncertainties another CNN was trained todetect local segmentation failures that potentially need correction by anexpert. Finally, manual correction of the detected regions was simulated. Usingpublicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact ofCNN architecture and loss function for segmentation, and the uncertaintymeasure was investigated. Performance was evaluated using the Dice coefficientand 3D Hausdorff distance between manual and automatic segmentation. Theexperiments reveal that combining automatic segmentation with simulated manualcorrection of detected segmentation failures leads to statistically significantperformance increase.

Automatic segmentation with detection of local segmentation failures in cardiac MRI