Self-supervised pretraining (SSP) has shown promising results in learning from large unlabeled datasets and, thus, could be useful for automated cardiovascular magnetic resonance (CMR) short-axis cine segmentation. However, inconsistent reports of the benefits of SSP for segmentation have made it difficult to apply SSP to CMR. Therefore, this study aimed to evaluate SSP methods for CMR cine segmentation. To this end, short-axis cine stacks of 296 subjects (90618 2D slices) were used for unlabeled pretraining with four SSP methods; SimCLR, positional contrastive learning, DINO, and masked image modeling (MIM). Subsets of varying numbers of subjects were used for supervised fine-tuning of 2D models for each SSP method, as well as to train a 2D baseline model from scratch. The fine-tuned models were compared to the baseline using the 3D Dice similarity coefficient (DSC) in a test dataset of 140 subjects. The SSP methods showed no performance gains with the largest supervised fine-tuning subset compared to the baseline (DSC = 0.89). When only 10 subjects (231 2D slices) are available for supervised training, SSP using MIM (DSC = 0.86) improves over training from scratch (DSC = 0.82). This study found that SSP is valuable for CMR cine segmentation when labeled training data is scarce, but does not aid state-of-the-art deep learning methods when ample labeled data is available. Moreover, the choice of SSP method is important. The code is publicly available at: https://github.com/q-cardIA/ssp-cmr-cine-segmentation

翻译：自监督预训练（SSP）在从大规模无标签数据集中学习方面已展现出良好前景，因此可能对自动化心血管磁共振（CMR）短轴电影序列分割具有应用价值。然而，关于SSP对分割任务益处的报告存在不一致性，这导致将SSP应用于CMR领域存在困难。为此，本研究旨在评估用于CMR电影序列分割的SSP方法。研究中采用296名受试者（共90618张二维切片）的短轴电影序列堆栈，使用四种SSP方法进行无标签预训练：SimCLR、位置对比学习、DINO以及掩码图像建模（MIM）。随后，使用不同数量受试者的子集，对每种SSP方法进行二维模型的监督微调，并从头训练一个二维基线模型。在包含140名受试者的测试数据集中，通过三维Dice相似系数（DSC）将微调模型与基线模型进行比较。在使用最大监督微调子集时，SSP方法相比基线（DSC = 0.89）未表现出性能提升。当仅有10名受试者（231张二维切片）可用于监督训练时，采用MIM的SSP（DSC = 0.86）优于从头训练（DSC = 0.82）。本研究发现，当标注训练数据稀缺时，SSP对CMR电影序列分割具有重要价值；但在有充足标注数据可用时，SSP对当前最先进的深度学习方法并无助益。此外，SSP方法的选择至关重要。代码已公开于：https://github.com/q-cardIA/ssp-cmr-cine-segmentation