  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 structures allows doctors to assess the heart's condition and
devise treatments with greater precision and accuracy. However, achieving
accurate and reliable left ventricle segmentation is time-consuming and
challenging due to different reasons. Hence, clinicians often rely on
segmenting the left ventricular (LV) in two specific echocardiogram frames to
make a diagnosis. This limited coverage in manual LV segmentation poses a
challenge for developing automatic LV segmentation with high temporal
consistency, as the resulting dataset is typically annotated sparsely. In
response to this challenge, this work introduces SimLVSeg, a novel paradigm
that enables video-based networks for consistent LV segmentation from sparsely
annotated echocardiogram videos. SimLVSeg consists of self-supervised
pre-training with temporal masking, followed by weakly supervised learning
tailored for LV segmentation from sparse annotations. We demonstrate how
SimLVSeg outperforms the state-of-the-art solutions by achieving a 93.32%
(95%CI 93.21-93.43%) dice score on the largest 2D+time echocardiography dataset
(EchoNet-Dynamic) while being more efficient. SimLVSeg is compatible with two
types of video segmentation networks: 2D super image and 3D segmentation. To
show the effectiveness of our approach, we provide extensive ablation studies,
including pre-training settings and various deep learning backbones. We further
conduct an out-of-distribution test to showcase SimLVSeg's generalizability on
unseen distribution (CAMUS dataset). The code is publicly available at
https://github.com/fadamsyah/SimLVSeg.
