  Segmentation and measurement of cardiac chambers is critical in cardiac
ultrasound but is laborious and poorly reproducible. Neural networks can
assist, but supervised approaches require the same laborious manual
annotations. We built a pipeline for self-supervised (no manual labels)
segmentation combining computer vision, clinical domain knowledge, and deep
learning. We trained on 450 echocardiograms (93,000 images) and tested on 8,393
echocardiograms (4,476,266 images; mean 61 years, 51% female), using the
resulting segmentations to calculate biometrics. We also tested against
external images from an additional 10,030 patients with available manual
tracings of the left ventricle. r2 between clinically measured and
pipeline-predicted measurements were similar to reported inter-clinician
variation and comparable to supervised learning across several different
measurements (r2 0.56-0.84). Average accuracy for detecting abnormal chamber
size and function was 0.85 (range 0.71-0.97) compared to clinical measurements.
A subset of test echocardiograms (n=553) had corresponding cardiac MRIs, where
MRI is the gold standard. Correlation between pipeline and MRI measurements was
similar to that between clinical echocardiogram and MRI. Finally, the pipeline
accurately segments the left ventricle with an average Dice score of 0.89 (95%
CI [0.89]) in the external, manually labeled dataset. Our results demonstrate a
manual-label free, clinically valid, and highly scalable method for
segmentation from ultrasound, a noisy but globally important imaging modality.
