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.

翻译：超声心动图已成为心脏健康评估不可或缺的临床影像模态。从计算射血分数等生物标志物到患者心力衰竭概率评估，心脏及其结构的精确分割使医生能够以更高的精确度规划并执行治疗方案。然而，受多种因素制约，实现准确且鲁棒的左心室分割既耗时又充满挑战。本研究提出一种新方法，用于从稀疏标注的超声心动图视频中实现一致的左心室分割。我们通过以下步骤实现该目标：(1) 采用时间掩码的自监督学习，以及(2) 弱监督训练。我们探究了两种分割方法：三维分割与新型二维超图像。实验表明，所提方法在大型数据集（EchoNet-Dynamic）上达到93.32%（95%置信区间：93.21-93.43%）的Dice分数，同时具备更高效率，性能优于现有最优方案。为验证方法的有效性，我们开展了全面的消融研究，涵盖预训练设置与多种深度学习骨干网络。此外，我们探讨了所提方法通过将未标注帧纳入训练过程实现高数据利用率的机制。为助力医学人工智能领域发展，经录用后，包含完整源代码的解决方案将向公众开放。