Abstract 15393: Automatic Multi-structural Cardiac Segmentation of 2d Echocardiography With Convolutional Neural Networks

Introduction: The use of convolutional neural networks (CNN) to automatically segment the heart from echocardiography images has garnered recent attention, but generalizable performance segmenting multiple cardiac structures has not been demonstrated. The objective of the present work was to develop a 2D CNN model to automatically and accurately segment the left ventricular (LV) endo- and epicardial and left atrial (LA) endocardial surfaces from an independent, external clinical dataset. Methods: A modified U-net CNN was trained using 10-fold cross-validation and augmentation within the published CAMUS echocardiography dataset to segment the LV and LA from apical 2- and 4-chamber images. For external validation, this model was applied to 3,087 echocardiograms from Geisinger for which the physician-reported LV ejection fraction (EF) was within 10% of the reported bi-plane EF, signifying confidence in quality of the underlying images. LV end-diastolic and end-systolic volumes (EDV, ESV), LA volume (LAV), and LV mass (LVM) were estimated using Simpson’s bi-plane summation. We compared performance against two published segmentation models. Results: Our model agreed well with clinically-reported values, based on small median absolute errors in percent of clinical measures (MAE; Table 1) and biases, and narrow limits of agreement (mean bias/coefficient of variation (%): LV EDV 10.3/10.6, ESV 6.3/13.1, EF 0.5/6.3, LVM 13.9/10.1, and LAV 3.1/9.5). Moreover, the observed MAE for each metric was within the previously reported limits of inter-observer variability for 2D echo. Compared to two previously published models, our model exhibited smaller MAE in all measures tested (Table 1). Conclusions: Our model exhibits accurate, generalizable performance in multi-structural echocardiography segmentation, with accuracy meeting or exceeding current leading models. Such models hold great promise for translational research and precision medicine efforts.