Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, these models are still considered unreliable by clinicians due to unresolved issues concerning i) the temporal consistency of their predictions, and ii) their ability to generalize across datasets. In this context, we propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects. We introduce a new private dataset, named CARDINAL, of apical two-chamber and apical four-chamber sequences, with reference segmentation over the full cardiac cycle. We show that the proposed 3D nnU-Net outperforms alternative 2D and recurrent segmentation methods. We also report that the best models trained on CARDINAL, when tested on CAMUS without any fine-tuning, still manage to perform competitively with respect to prior methods. Overall, the experimental results suggest that with sufficient training data, 3D nnU-Net could become the first automated tool to finally meet the standards of an everyday clinical device.

翻译：基于深度学习的方法已引领超声心动图图像的自动分析，这得益于多个由专家标注的开放访问数据集的发布（CAMUS是最大的公共数据库之一）。然而，由于以下未解决的问题，临床医生仍认为这些模型不可靠：i）其预测的时间一致性，以及ii）其跨数据集的泛化能力。在此背景下，我们提出对当前医学/超声心动图图像分割中性能最佳的方法进行全面比较，特别关注时间一致性和跨数据集方面。我们引入一个新的私有数据集，命名为CARDINAL，包含心尖两腔和心尖四腔序列，并涵盖整个心动周期的参考分割。我们表明，所提出的3D nnU-Net优于替代的2D和循环分割方法。我们还报告，在CARDINAL上训练的最佳模型，当在CAMUS上测试且无需任何微调时，仍能与先前方法竞争。总体而言，实验结果表明，在充足训练数据下，3D nnU-Net可能成为首个最终满足日常临床设备标准的自动化工具。