  Bayesian Optimization (BO) is a well-studied hyperparameter tuning technique
that is more efficient than grid search for high-cost, high-parameter machine
learning problems. Echocardiography is a ubiquitous modality for evaluating
heart structure and function in cardiology. In this work, we use BO to optimize
the architectural and training-related hyperparameters of a previously
published deep fully convolutional neural network model for multi-structure
segmentation in echocardiography. In a fair comparison, the resulting model
outperforms this recent state-of-the-art on the annotated CAMUS dataset in both
apical two- and four-chamber echo views. We report mean Dice overlaps of 0.95,
0.96, and 0.93 on left ventricular (LV) endocardium, LV epicardium, and left
atrium respectively. We also observe significant improvement in derived
clinical indices, including smaller median absolute errors for LV end-diastolic
volume (4.9mL vs. 6.7), end-systolic volume (3.1mL vs. 5.2), and ejection
fraction (2.6% vs. 3.7); and much tighter limits of agreement, which were
already within inter-rater variability for non-contrast echo. These results
demonstrate the benefits of BO for echocardiography segmentation over a recent
state-of-the-art framework, although validation using large-scale independent
clinical data is required.
