  Echocardiography has become an indispensable clinical imaging modality for
general heart health assessment. From calculating biomarkers such as ejection
fraction to the probability of a patient's heart failure, accurate segmentation
of the heart and its structures allows doctors to plan and execute treatments
with greater precision and accuracy. However, achieving accurate and robust
left ventricle segmentation is time-consuming and challenging due to different
reasons. This work introduces a novel approach for consistent left ventricular
(LV) segmentation from sparsely annotated echocardiogram videos. We achieve
this through (1) self-supervised learning (SSL) using temporal masking followed
by (2) weakly supervised training. We investigate two different segmentation
approaches: 3D segmentation and a novel 2D superimage (SI). We demonstrate how
our proposed method outperforms the state-of-the-art solutions by achieving a
93.32% (95%CI 93.21-93.43%) dice score on a large-scale dataset
(EchoNet-Dynamic) while being more efficient. To show the effectiveness of our
approach, we provide extensive ablation studies, including pre-training
settings and various deep learning backbones. Additionally, we discuss how our
proposed methodology achieves high data utility by incorporating unlabeled
frames in the training process. To help support the AI in medicine community,
the complete solution with the source code will be made publicly available upon
acceptance.

Computer Vision and Pattern Recognition