Purpose: The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD).

Approach: A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets). LV structural parameters and LV ejection fraction (LVEF) were measured, and myocardial strains estimated with the radial point interpolation method. Myocardial classification validation was against quantization-based ground-truth with computations of accuracy, Dice score, average perpendicular distance (APD), Hausdorff-distance, and others. Additional validations were conducted with equivalence tests and Cronbach’s alpha (C-α) intraclass correlation coefficients between the FCN and a vendor tool on chamber quantification and myocardial strain computations.

Results: Myocardial classification results against ground-truth were Dice=0.89, APD=2.4 mm, and accuracy=97% for the validation set and Dice=0.90, APD=2.5 mm, and accuracy=97% for the test set. The confidence intervals (CI) and two one-sided t-test results of equivalence tests between the FCN and vendor-tool were CI=−1.36% to 2.42%, p-value < 0.001 for LVEF (58±5% versus 57±6%), and CI=−0.71% to 0.63%, p-value < 0.001 for longitudinal strain (−15±2% versus −15±3%).

Conclusions: The validation results were found equivalent to the vendor tool-based parameter estimates, which show that accurate LV chamber quantification followed by strain analysis for CTRCD investigation can be achieved with our proposed FCN methodology.