Abstract: 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 endocardium, epicardium, and left atrium respectively. We also observe significant improvement in derived clinical indices, including smaller median absolute errors for left ventricular 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. While these results demonstrate the benefits of BO for echocardiography segmentation over even a recent state-of-the-art framework, they must still be validated against large-scale independent clinical data.
Paper (pdf, preprint), and poster.
Local SVUR'21 Symposium: video presentation.
