  Left-ventricular ejection fraction (LVEF) is an important indicator of heart
failure. Existing methods for LVEF estimation from video require large amounts
of annotated data to achieve high performance, e.g. using 10,030 labeled
echocardiogram videos to achieve mean absolute error (MAE) of 4.10. Labeling
these videos is time-consuming however and limits potential downstream
applications to other heart diseases. This paper presents the first
semi-supervised approach for LVEF prediction. Unlike general video prediction
tasks, LVEF prediction is specifically related to changes in the left ventricle
(LV) in echocardiogram videos. By incorporating knowledge learned from
predicting LV segmentations into LVEF regression, we can provide additional
context to the model for better predictions. To this end, we propose a novel
Cyclical Self-Supervision (CSS) method for learning video-based LV
segmentation, which is motivated by the observation that the heartbeat is a
cyclical process with temporal repetition. Prediction masks from our
segmentation model can then be used as additional input for LVEF regression to
provide spatial context for the LV region. We also introduce teacher-student
distillation to distill the information from LV segmentation masks into an
end-to-end LVEF regression model that only requires video inputs. Results show
our method outperforms alternative semi-supervised methods and can achieve MAE
of 4.17, which is competitive with state-of-the-art supervised performance,
using half the number of labels. Validation on an external dataset also shows
improved generalization ability from using our method. Our code is available at
https://github.com/xmed-lab/CSS-SemiVideo.
