Deep Learning Segmentation and Quantification of the Left Ventricle from the Parasternal Short-Axis View in...

Quantitative measurements remain under-used in echocardiography due to measurement variability and time consumption. This study aimed to develop a deep learning (DL) pipeline for the automatic extraction of clinically relevant quantitative measurements from the parasternal short-axis (PSAX) view.

We trained an nnU-Net model to segment the left ventricle (LV) lumen and myocardium in PSAX images. Based on end-diastole (ED) and end-systole (ES) frames in the echocardiograms, we calculated the LV lumen area, LV fractional area change, mean wall thickness (MWT) and global circumferential strain. Segmentations and measurements were validated through comparison with two manual observers.

Compared with manual references, the nnU-Net model achieved a high grade of similarity for the LV lumen and myocardium, with Dice coefficients of 0.93±0.04 and 0.84±0.08, and 95th percentile Hausdorff distances of 3.2±1.9mm and 3.4±1.7mm, respectively. The Dice coefficients and 95th percentile Hausdorff distances were on par or better for the evaluation dataset. DL-based measurements were in line with interobserver precision and variability. Automatic timing of ED and ES frames based on echocardiograms and LV lumen area produced similar results to using manual timing by experts. The subject-level feasibility was 90.4%. Furthermore, DL-based measurement of MWT at ED differentiated subjects with and without hypertension (p<0.001).

Our DL-based measurement pipeline for PSAX achieved performance comparable to expert annotators, positioning it as a possible substitute for tedious manual measurements. DL-derived MWT was able to differentiate hypertensive from non-hypertensive subjects, which indicates the potential clinical utility of fully automated PSAX measurements.